From ab11d96e7dd493814400e88b5d6d758a6a87652f Mon Sep 17 00:00:00 2001
From: Bryce Meyer <bryce13950@gmail.com>
Date: Sun, 22 Oct 2023 23:33:13 +0200
Subject: [PATCH 01/25] added main demo test

---
 demos/Main_Demo.ipynb | 210 +++++++++++++++++-------------------------
 makefile              |   1 +
 2 files changed, 86 insertions(+), 125 deletions(-)

diff --git a/demos/Main_Demo.ipynb b/demos/Main_Demo.ipynb
index d97f23867..fe73176cd 100644
--- a/demos/Main_Demo.ipynb
+++ b/demos/Main_Demo.ipynb
@@ -47,38 +47,31 @@
             "cell_type": "code",
             "execution_count": 1,
             "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Running as a Jupyter notebook - intended for development only!\n"
-                    ]
-                }
-            ],
+            "outputs": [],
             "source": [
-                "# Janky code to do different setup when run in a Colab notebook vs VSCode\n",
                 "DEVELOPMENT_MODE = False\n",
+                "# Detect if we're running in Google Colab\n",
                 "try:\n",
                 "    import google.colab\n",
                 "    IN_COLAB = True\n",
                 "    print(\"Running as a Colab notebook\")\n",
-                "    %pip install git+https://github.com/neelnanda-io/TransformerLens.git\n",
-                "    %pip install circuitsvis\n",
-                "    \n",
-                "    # PySvelte is an unmaintained visualization library, use it as a backup if circuitsvis isn't working\n",
-                "    # # Install another version of node that makes PySvelte work way faster\n",
-                "    # !curl -fsSL https://deb.nodesource.com/setup_16.x | sudo -E bash -; sudo apt-get install -y nodejs\n",
-                "    # %pip install git+https://github.com/neelnanda-io/PySvelte.git\n",
                 "except:\n",
                 "    IN_COLAB = False\n",
-                "    print(\"Running as a Jupyter notebook - intended for development only!\")\n",
-                "    from IPython import get_ipython\n",
                 "\n",
-                "    ipython = get_ipython()\n",
-                "    # Code to automatically update the HookedTransformer code as its edited without restarting the kernel\n",
-                "    ipython.magic(\"load_ext autoreload\")\n",
-                "    ipython.magic(\"autoreload 2\")"
+                "# Install if in Colab\n",
+                "if IN_COLAB:\n",
+                "    %pip install transformer_lens\n",
+                "    %pip install circuitsvis\n",
+                "    # Install a faster Node version\n",
+                "    !curl -fsSL https://deb.nodesource.com/setup_16.x | sudo -E bash -; sudo apt-get install -y nodejs  # noqa\n",
+                "\n",
+                "# Hot reload in development mode & not running on the CD\n",
+                "if not IN_COLAB:\n",
+                "    from IPython import get_ipython\n",
+                "    ip = get_ipython()\n",
+                "    if not ip.extension_manager.loaded:\n",
+                "        ip.extension_manager.load('autoreload')\n",
+                "        %autoreload 2"
             ]
         },
         {
@@ -112,18 +105,18 @@
                 {
                     "data": {
                         "text/html": [
-                            "<div id=\"circuits-vis-9bef902a-630d\" style=\"margin: 15px 0;\"/>\n",
+                            "<div id=\"circuits-vis-84d5f322-d9bc\" style=\"margin: 15px 0;\"/>\n",
                             "    <script crossorigin type=\"module\">\n",
-                            "    import { render, Hello } from \"https://unpkg.com/circuitsvis@1.38.1/dist/cdn/esm.js\";\n",
+                            "    import { render, Hello } from \"https://unpkg.com/circuitsvis@1.42.0/dist/cdn/esm.js\";\n",
                             "    render(\n",
-                            "      \"circuits-vis-9bef902a-630d\",\n",
+                            "      \"circuits-vis-84d5f322-d9bc\",\n",
                             "      Hello,\n",
                             "      {\"name\": \"Neel\"}\n",
                             "    )\n",
                             "    </script>"
                         ],
                         "text/plain": [
-                            "<circuitsvis.utils.render.RenderedHTML at 0x104f16580>"
+                            "<circuitsvis.utils.render.RenderedHTML at 0xffffac0c6c70>"
                         ]
                     },
                     "execution_count": 3,
@@ -199,7 +192,7 @@
                 {
                     "data": {
                         "text/plain": [
-                            "<torch.autograd.grad_mode.set_grad_enabled at 0x168924c10>"
+                            "<torch.autograd.grad_mode.set_grad_enabled at 0xffff5fdcab20>"
                         ]
                     },
                     "execution_count": 6,
@@ -419,18 +412,18 @@
                 {
                     "data": {
                         "text/html": [
-                            "<div id=\"circuits-vis-417711b4-7336\" style=\"margin: 15px 0;\"/>\n",
+                            "<div id=\"circuits-vis-01fa67fc-f7e6\" style=\"margin: 15px 0;\"/>\n",
                             "    <script crossorigin type=\"module\">\n",
-                            "    import { render, AttentionPatterns } from \"https://unpkg.com/circuitsvis@1.38.1/dist/cdn/esm.js\";\n",
+                            "    import { render, AttentionPatterns } from \"https://unpkg.com/circuitsvis@1.42.0/dist/cdn/esm.js\";\n",
                             "    render(\n",
-                            "      \"circuits-vis-417711b4-7336\",\n",
+                            "      \"circuits-vis-01fa67fc-f7e6\",\n",
                             "      AttentionPatterns,\n",
-                            "      {\"tokens\": [\"<|endoftext|>\", \"Natural\", \" language\", \" processing\", \" tasks\", \",\", \" such\", \" as\", \" question\", \" answering\", \",\", \" machine\", \" translation\", \",\", \" reading\", \" comprehension\", \",\", \" and\", \" summar\", \"ization\", \",\", \" are\", \" typically\", \" approached\", \" with\", \" supervised\", \" learning\", \" on\", \" tasks\", \"pe\", \"cific\", \" datasets\", \".\"], \"attention\": [[[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.9639418721199036, 0.036058153957128525, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.8389371633529663, 0.11828794330358505, 0.04277484491467476, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 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                         "text/plain": [
-                            "<circuitsvis.utils.render.RenderedHTML at 0x177430a90>"
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                     },
                     "execution_count": 13,
@@ -614,20 +607,10 @@
             "execution_count": 16,
             "metadata": {},
             "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
-                        "To disable this warning, you can either:\n",
-                        "\t- Avoid using `tokenizers` before the fork if possible\n",
-                        "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
-                    ]
-                },
                 {
                     "data": {
                         "application/vnd.jupyter.widget-view+json": {
-                            "model_id": "b93d6b6e8c37495f84b7a00f2caf81c3",
+                            "model_id": "267fc32b322044f8896d7ac5b935ea5d",
                             "version_major": 2,
                             "version_minor": 0
                         },
@@ -681,47 +664,24 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 23,
+            "execution_count": 17,
             "metadata": {},
             "outputs": [
                 {
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+                    "ename": "ModuleNotFoundError",
+                    "evalue": "No module named 'matplotlib'",
+                    "output_type": "error",
+                    "traceback": [
+                        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+                        "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+                        "\u001b[1;32m/workspaces/TransformerLens/demos/Main_Demo.ipynb Cell 38\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> <a href='vscode-notebook-cell://dev-container%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_Demo.ipynb#X52sdnNjb2RlLXJlbW90ZQ%3D%3D?line=0'>1</a>\u001b[0m get_ipython()\u001b[39m.\u001b[39;49mrun_line_magic(\u001b[39m'\u001b[39;49m\u001b[39mmatplotlib\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39m'\u001b[39;49m\u001b[39minline\u001b[39;49m\u001b[39m'\u001b[39;49m)\n\u001b[1;32m      <a href='vscode-notebook-cell://dev-container%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_Demo.ipynb#X52sdnNjb2RlLXJlbW90ZQ%3D%3D?line=1'>2</a>\u001b[0m \u001b[39m# Add the index to the end of the label, because plotly doesn't like duplicate labels\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell://dev-container%2B7b22686f737450617468223a222f55736572732f62727963652f50726f6a656374732f4c696e67776176652f5472616e73666f726d65724c656e73222c226c6f63616c446f636b6572223a66616c73652c2273657474696e6773223a7b22636f6e74657874223a226465736b746f702d6c696e7578227d2c22636f6e66696746696c65223a7b22246d6964223a312c22667350617468223a222f55736572732f62727963652f50726f6a656374732f4c696e67776176652f5472616e73666f726d65724c656e732f2e646576636f6e7461696e65722f646576636f6e7461696e65722e6a736f6e222c2265787465726e616c223a2266696c653a2f2f2f55736572732f62727963652f50726f6a656374732f4c696e67776176652f5472616e73666f726d65724c656e732f2e646576636f6e7461696e65722f646576636f6e7461696e65722e6a736f6e222c2270617468223a222f55736572732f62727963652f50726f6a656374732f4c696e67776176652f5472616e73666f726d65724c656e732f2e646576636f6e7461696e65722f646576636f6e7461696e65722e6a736f6e222c22736368656d65223a2266696c65227d7d/workspaces/TransformerLens/demos/Main_Demo.ipynb#X52sdnNjb2RlLXJlbW90ZQ%3D%3D?line=2'>3</a>\u001b[0m token_labels \u001b[39m=\u001b[39m [\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00mtoken\u001b[39m}\u001b[39;00m\u001b[39m_\u001b[39m\u001b[39m{\u001b[39;00mindex\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m \u001b[39mfor\u001b[39;00m index, token \u001b[39min\u001b[39;00m \u001b[39menumerate\u001b[39m(model\u001b[39m.\u001b[39mto_str_tokens(clean_tokens))]\n",
+                        "File \u001b[0;32m/workspaces/TransformerLens/.venv/lib/python3.8/site-packages/IPython/core/interactiveshell.py:2417\u001b[0m, in \u001b[0;36mInteractiveShell.run_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m   2415\u001b[0m     kwargs[\u001b[39m'\u001b[39m\u001b[39mlocal_ns\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mget_local_scope(stack_depth)\n\u001b[1;32m   2416\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mbuiltin_trap:\n\u001b[0;32m-> 2417\u001b[0m     result \u001b[39m=\u001b[39m fn(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   2419\u001b[0m \u001b[39m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m   2420\u001b[0m \u001b[39m# when using magics with decodator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m   2421\u001b[0m \u001b[39m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m   2422\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mgetattr\u001b[39m(fn, magic\u001b[39m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[39mFalse\u001b[39;00m):\n",
+                        "File \u001b[0;32m/workspaces/TransformerLens/.venv/lib/python3.8/site-packages/IPython/core/magics/pylab.py:99\u001b[0m, in \u001b[0;36mPylabMagics.matplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m     97\u001b[0m     \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mAvailable matplotlib backends: \u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m\"\u001b[39m \u001b[39m%\u001b[39m backends_list)\n\u001b[1;32m     98\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m---> 99\u001b[0m     gui, backend \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mshell\u001b[39m.\u001b[39;49menable_matplotlib(args\u001b[39m.\u001b[39;49mgui\u001b[39m.\u001b[39;49mlower() \u001b[39mif\u001b[39;49;00m \u001b[39misinstance\u001b[39;49m(args\u001b[39m.\u001b[39;49mgui, \u001b[39mstr\u001b[39;49m) \u001b[39melse\u001b[39;49;00m args\u001b[39m.\u001b[39;49mgui)\n\u001b[1;32m    100\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_show_matplotlib_backend(args\u001b[39m.\u001b[39mgui, backend)\n",
+                        "File \u001b[0;32m/workspaces/TransformerLens/.venv/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3588\u001b[0m, in \u001b[0;36mInteractiveShell.enable_matplotlib\u001b[0;34m(self, gui)\u001b[0m\n\u001b[1;32m   3567\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39menable_matplotlib\u001b[39m(\u001b[39mself\u001b[39m, gui\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m):\n\u001b[1;32m   3568\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"Enable interactive matplotlib and inline figure support.\u001b[39;00m\n\u001b[1;32m   3569\u001b[0m \n\u001b[1;32m   3570\u001b[0m \u001b[39m    This takes the following steps:\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   3586\u001b[0m \u001b[39m        display figures inline.\u001b[39;00m\n\u001b[1;32m   3587\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3588\u001b[0m     \u001b[39mfrom\u001b[39;00m \u001b[39mmatplotlib_inline\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mbackend_inline\u001b[39;00m \u001b[39mimport\u001b[39;00m configure_inline_support\n\u001b[1;32m   3590\u001b[0m     \u001b[39mfrom\u001b[39;00m \u001b[39mIPython\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mcore\u001b[39;00m \u001b[39mimport\u001b[39;00m pylabtools \u001b[39mas\u001b[39;00m pt\n\u001b[1;32m   3591\u001b[0m     gui, backend \u001b[39m=\u001b[39m pt\u001b[39m.\u001b[39mfind_gui_and_backend(gui, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpylab_gui_select)\n",
+                        "File \u001b[0;32m/workspaces/TransformerLens/.venv/lib/python3.8/site-packages/matplotlib_inline/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m \u001b[39mimport\u001b[39;00m backend_inline, config  \u001b[39m# noqa\u001b[39;00m\n\u001b[1;32m      2\u001b[0m __version__ \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m0.1.6\u001b[39m\u001b[39m\"\u001b[39m  \u001b[39m# noqa\u001b[39;00m\n",
+                        "File \u001b[0;32m/workspaces/TransformerLens/.venv/lib/python3.8/site-packages/matplotlib_inline/backend_inline.py:6\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[39m\"\"\"A matplotlib backend for publishing figures via display_data\"\"\"\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[39m# Copyright (c) IPython Development Team.\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \u001b[39m# Distributed under the terms of the BSD 3-Clause License.\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mmatplotlib\u001b[39;00m\n\u001b[1;32m      7\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mmatplotlib\u001b[39;00m \u001b[39mimport\u001b[39;00m colors\n\u001b[1;32m      8\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mmatplotlib\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mbackends\u001b[39;00m \u001b[39mimport\u001b[39;00m backend_agg\n",
+                        "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'"
+                    ]
                 }
             ],
             "source": [
@@ -768,7 +728,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 20,
+            "execution_count": null,
             "metadata": {},
             "outputs": [
                 {
@@ -840,7 +800,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 19,
+            "execution_count": null,
             "metadata": {},
             "outputs": [
                 {
@@ -924,7 +884,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 20,
+            "execution_count": null,
             "metadata": {},
             "outputs": [
                 {
@@ -1011,7 +971,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 21,
+            "execution_count": null,
             "metadata": {},
             "outputs": [
                 {
@@ -1216,7 +1176,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 22,
+            "execution_count": null,
             "metadata": {},
             "outputs": [
                 {
@@ -1253,7 +1213,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 23,
+            "execution_count": null,
             "metadata": {},
             "outputs": [
                 {
@@ -1291,7 +1251,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 24,
+            "execution_count": null,
             "metadata": {},
             "outputs": [
                 {
@@ -1377,7 +1337,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 25,
+            "execution_count": null,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -1387,7 +1347,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 26,
+            "execution_count": null,
             "metadata": {},
             "outputs": [
                 {
@@ -1461,7 +1421,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 27,
+            "execution_count": null,
             "metadata": {},
             "outputs": [
                 {
@@ -1522,7 +1482,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 28,
+            "execution_count": null,
             "metadata": {},
             "outputs": [
                 {
@@ -1548,7 +1508,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 29,
+            "execution_count": null,
             "metadata": {},
             "outputs": [
                 {
@@ -1581,7 +1541,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 30,
+            "execution_count": null,
             "metadata": {},
             "outputs": [
                 {
@@ -1618,7 +1578,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 31,
+            "execution_count": null,
             "metadata": {},
             "outputs": [
                 {
@@ -1651,7 +1611,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 32,
+            "execution_count": null,
             "metadata": {},
             "outputs": [
                 {
@@ -1682,7 +1642,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 33,
+            "execution_count": null,
             "metadata": {},
             "outputs": [
                 {
@@ -1708,7 +1668,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 34,
+            "execution_count": null,
             "metadata": {},
             "outputs": [
                 {
@@ -1740,7 +1700,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 35,
+            "execution_count": null,
             "metadata": {},
             "outputs": [
                 {
@@ -1784,7 +1744,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 36,
+            "execution_count": null,
             "metadata": {},
             "outputs": [
                 {
@@ -1818,7 +1778,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 37,
+            "execution_count": null,
             "metadata": {},
             "outputs": [
                 {
@@ -1851,7 +1811,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 38,
+            "execution_count": null,
             "metadata": {},
             "outputs": [
                 {
@@ -1904,7 +1864,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 39,
+            "execution_count": null,
             "metadata": {},
             "outputs": [
                 {
@@ -1939,7 +1899,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 40,
+            "execution_count": null,
             "metadata": {},
             "outputs": [
                 {
@@ -1977,7 +1937,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 41,
+            "execution_count": null,
             "metadata": {},
             "outputs": [
                 {
@@ -2008,7 +1968,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 42,
+            "execution_count": null,
             "metadata": {},
             "outputs": [
                 {
@@ -2051,7 +2011,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 43,
+            "execution_count": null,
             "metadata": {},
             "outputs": [
                 {
@@ -2069,7 +2029,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 44,
+            "execution_count": null,
             "metadata": {},
             "outputs": [
                 {
@@ -2089,7 +2049,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 45,
+            "execution_count": null,
             "metadata": {},
             "outputs": [
                 {
@@ -2146,7 +2106,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 46,
+            "execution_count": null,
             "metadata": {},
             "outputs": [
                 {
@@ -2202,7 +2162,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 47,
+            "execution_count": null,
             "metadata": {},
             "outputs": [
                 {
@@ -2220,7 +2180,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 48,
+            "execution_count": null,
             "metadata": {},
             "outputs": [
                 {
@@ -2240,7 +2200,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 49,
+            "execution_count": null,
             "metadata": {},
             "outputs": [
                 {
@@ -2297,7 +2257,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 50,
+            "execution_count": null,
             "metadata": {},
             "outputs": [
                 {
@@ -2346,7 +2306,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 51,
+            "execution_count": null,
             "metadata": {},
             "outputs": [
                 {
@@ -2383,7 +2343,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 52,
+            "execution_count": null,
             "metadata": {},
             "outputs": [
                 {
@@ -2461,7 +2421,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 53,
+            "execution_count": null,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -2519,7 +2479,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 54,
+            "execution_count": null,
             "metadata": {},
             "outputs": [
                 {
@@ -2554,7 +2514,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 55,
+            "execution_count": null,
             "metadata": {},
             "outputs": [
                 {
@@ -2621,7 +2581,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 56,
+            "execution_count": null,
             "metadata": {},
             "outputs": [
                 {
@@ -2861,7 +2821,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 57,
+            "execution_count": null,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -2884,7 +2844,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 58,
+            "execution_count": null,
             "metadata": {},
             "outputs": [
                 {
@@ -2923,7 +2883,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 59,
+            "execution_count": null,
             "metadata": {},
             "outputs": [
                 {
@@ -2987,7 +2947,7 @@
             "name": "python",
             "nbconvert_exporter": "python",
             "pygments_lexer": "ipython3",
-            "version": "3.9.14"
+            "version": "3.8.10"
         },
         "orig_nbformat": 4,
         "vscode": {
diff --git a/makefile b/makefile
index a17630f9b..08f2fb9b8 100644
--- a/makefile
+++ b/makefile
@@ -18,6 +18,7 @@ docstring-test:
 	poetry run pytest transformer_lens/
 
 notebook-test:
+	poetry run pytest demos/Main_Demo.ipynb 
 	poetry run pytest demos/Exploratory_Analysis_Demo.ipynb
 
 test:

From 56077135524ec1c37f690f40d1843e906ecc16e9 Mon Sep 17 00:00:00 2001
From: Bryce Meyer <bryce13950@gmail.com>
Date: Sun, 22 Oct 2023 23:43:23 +0200
Subject: [PATCH 02/25] skipped plain text comparison

---
 demos/conftest.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/demos/conftest.py b/demos/conftest.py
index 2cd3d5a3e..08890c167 100644
--- a/demos/conftest.py
+++ b/demos/conftest.py
@@ -3,6 +3,7 @@ def pytest_collectstart(collector):
     if collector.fspath and collector.fspath.ext == ".ipynb":
         collector.skip_compare += (
             "text/html",
+            "text/plain",
             "application/javascript",
             "application/vnd.plotly.v1+json",  # Plotly
         )

From eb354d9f6abde15f523b1a2a990ad96c696df052 Mon Sep 17 00:00:00 2001
From: Bryce Meyer <bryce13950@gmail.com>
Date: Sun, 22 Oct 2023 23:54:47 +0200
Subject: [PATCH 03/25] installed matplotlib

---
 demos/Main_Demo.ipynb | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/demos/Main_Demo.ipynb b/demos/Main_Demo.ipynb
index fe73176cd..9f38f2828 100644
--- a/demos/Main_Demo.ipynb
+++ b/demos/Main_Demo.ipynb
@@ -49,6 +49,7 @@
             "metadata": {},
             "outputs": [],
             "source": [
+                "import os\n",
                 "DEVELOPMENT_MODE = False\n",
                 "# Detect if we're running in Google Colab\n",
                 "try:\n",
@@ -57,6 +58,7 @@
                 "    print(\"Running as a Colab notebook\")\n",
                 "except:\n",
                 "    IN_COLAB = False\n",
+                "IN_GITHUB = os.getenv(\"GITHUB_ACTIONS\") == \"true\"\n",
                 "\n",
                 "# Install if in Colab\n",
                 "if IN_COLAB:\n",
@@ -71,7 +73,11 @@
                 "    ip = get_ipython()\n",
                 "    if not ip.extension_manager.loaded:\n",
                 "        ip.extension_manager.load('autoreload')\n",
-                "        %autoreload 2"
+                "        %autoreload 2\n",
+                "        \n",
+                "if IN_GITHUB:\n",
+                "    %pip install matplotlib\n",
+                "    "
             ]
         },
         {

From 17aed200ee7e8e5a04e4e344954616e34713befd Mon Sep 17 00:00:00 2001
From: Bryce Meyer <bryce13950@gmail.com>
Date: Mon, 23 Oct 2023 00:58:11 +0200
Subject: [PATCH 04/25] added demo to scripts

---
 docs/make_docs.py    | 5 ++++-
 docs/source/index.md | 1 +
 2 files changed, 5 insertions(+), 1 deletion(-)

diff --git a/docs/make_docs.py b/docs/make_docs.py
index 22498452d..ea6c41fba 100644
--- a/docs/make_docs.py
+++ b/docs/make_docs.py
@@ -116,7 +116,10 @@ def generate_model_table(_app: Optional[Any] = None):
 def copy_demos(_app: Optional[Any] = None):
     """Copy demo notebooks to the generated directory."""
     copy_to_dir = GENERATED_DIR / "demos"
-    notebooks_to_copy = ["Exploratory_Analysis_Demo.ipynb"]
+    notebooks_to_copy = [
+        "Exploratory_Analysis_Demo.ipynb",
+        "Main_Demo.ipynb",
+    ]
 
     if copy_to_dir.exists():
         shutil.rmtree(copy_to_dir)
diff --git a/docs/source/index.md b/docs/source/index.md
index f478501e9..09b00f20f 100644
--- a/docs/source/index.md
+++ b/docs/source/index.md
@@ -42,6 +42,7 @@ generated/model_properties_table.md
 content/tutorials
 content/citation
 content/contributing
+generated/demos/Main_Demo
 generated/demos/Exploratory_Analysis_Demo
 ```
 

From f4fdcc9c6becd687d12d08fcfe426f04fcbf5dd4 Mon Sep 17 00:00:00 2001
From: Bryce Meyer <bryce13950@gmail.com>
Date: Mon, 23 Oct 2023 13:49:52 +0200
Subject: [PATCH 05/25] added matlib plot to jupyter group and removed plain
 text mime type

---
 demos/Main_Demo.ipynb |   4 -
 demos/conftest.py     |   1 -
 poetry.lock           | 494 +++++++++++++++++++++++++++++++++++++++++-
 pyproject.toml        |   1 +
 4 files changed, 494 insertions(+), 6 deletions(-)

diff --git a/demos/Main_Demo.ipynb b/demos/Main_Demo.ipynb
index 9f38f2828..fd9c73303 100644
--- a/demos/Main_Demo.ipynb
+++ b/demos/Main_Demo.ipynb
@@ -58,7 +58,6 @@
                 "    print(\"Running as a Colab notebook\")\n",
                 "except:\n",
                 "    IN_COLAB = False\n",
-                "IN_GITHUB = os.getenv(\"GITHUB_ACTIONS\") == \"true\"\n",
                 "\n",
                 "# Install if in Colab\n",
                 "if IN_COLAB:\n",
@@ -74,9 +73,6 @@
                 "    if not ip.extension_manager.loaded:\n",
                 "        ip.extension_manager.load('autoreload')\n",
                 "        %autoreload 2\n",
-                "        \n",
-                "if IN_GITHUB:\n",
-                "    %pip install matplotlib\n",
                 "    "
             ]
         },
diff --git a/demos/conftest.py b/demos/conftest.py
index 08890c167..2cd3d5a3e 100644
--- a/demos/conftest.py
+++ b/demos/conftest.py
@@ -3,7 +3,6 @@ def pytest_collectstart(collector):
     if collector.fspath and collector.fspath.ext == ".ipynb":
         collector.skip_compare += (
             "text/html",
-            "text/plain",
             "application/javascript",
             "application/vnd.plotly.v1+json",  # Plotly
         )
diff --git a/poetry.lock b/poetry.lock
index ca664637b..c5db5081f 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -715,6 +715,142 @@ lint = ["black (>=22.6.0)", "mdformat (>0.7)", "mdformat-gfm (>=0.3.5)", "ruff (
 test = ["pytest"]
 typing = ["mypy (>=0.990)"]
 
+[[package]]
+name = "contourpy"
+version = "1.1.0"
+description = "Python library for calculating contours of 2D quadrilateral grids"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "contourpy-1.1.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:89f06eff3ce2f4b3eb24c1055a26981bffe4e7264acd86f15b97e40530b794bc"},
+    {file = "contourpy-1.1.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:dffcc2ddec1782dd2f2ce1ef16f070861af4fb78c69862ce0aab801495dda6a3"},
+    {file = "contourpy-1.1.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:25ae46595e22f93592d39a7eac3d638cda552c3e1160255258b695f7b58e5655"},
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+]
+
+[package.dependencies]
+numpy = ">=1.16"
+
+[package.extras]
+bokeh = ["bokeh", "selenium"]
+docs = ["furo", "sphinx-copybutton"]
+mypy = ["contourpy[bokeh,docs]", "docutils-stubs", "mypy (==1.2.0)", "types-Pillow"]
+test = ["Pillow", "contourpy[test-no-images]", "matplotlib"]
+test-no-images = ["pytest", "pytest-cov", "wurlitzer"]
+
+[[package]]
+name = "contourpy"
+version = "1.1.1"
+description = "Python library for calculating contours of 2D quadrilateral grids"
+optional = false
+python-versions = ">=3.8"
+files = [
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+
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 [package.extras]
 toml = ["tomli"]
 
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 [[package]]
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 [[package]]
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+    {file = "Pillow-10.1.0-pp39-pypy39_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:1fafabe50a6977ac70dfe829b2d5735fd54e190ab55259ec8aea4aaea412fa0b"},
+    {file = "Pillow-10.1.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:3b834f4b16173e5b92ab6566f0473bfb09f939ba14b23b8da1f54fa63e4b623f"},
+    {file = "Pillow-10.1.0.tar.gz", hash = "sha256:e6bf8de6c36ed96c86ea3b6e1d5273c53f46ef518a062464cd7ef5dd2cf92e38"},
+]
+
+[package.extras]
+docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-removed-in", "sphinxext-opengraph"]
+tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
+
 [[package]]
 name = "pkgutil-resolve-name"
 version = "1.3.10"
@@ -3063,6 +3531,20 @@ files = [
 [package.extras]
 plugins = ["importlib-metadata"]
 
+[[package]]
+name = "pyparsing"
+version = "3.1.1"
+description = "pyparsing module - Classes and methods to define and execute parsing grammars"
+optional = false
+python-versions = ">=3.6.8"
+files = [
+    {file = "pyparsing-3.1.1-py3-none-any.whl", hash = "sha256:32c7c0b711493c72ff18a981d24f28aaf9c1fb7ed5e9667c9e84e3db623bdbfb"},
+    {file = "pyparsing-3.1.1.tar.gz", hash = "sha256:ede28a1a32462f5a9705e07aea48001a08f7cf81a021585011deba701581a0db"},
+]
+
+[package.extras]
+diagrams = ["jinja2", "railroad-diagrams"]
+
 [[package]]
 name = "pytest"
 version = "7.4.2"
@@ -3208,6 +3690,7 @@ files = [
     {file = "PyYAML-6.0.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:69b023b2b4daa7548bcfbd4aa3da05b3a74b772db9e23b982788168117739938"},
     {file = "PyYAML-6.0.1-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:81e0b275a9ecc9c0c0c07b4b90ba548307583c125f54d5b6946cfee6360c733d"},
     {file = "PyYAML-6.0.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ba336e390cd8e4d1739f42dfe9bb83a3cc2e80f567d8805e11b46f4a943f5515"},
+    {file = "PyYAML-6.0.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:326c013efe8048858a6d312ddd31d56e468118ad4cdeda36c719bf5bb6192290"},
     {file = "PyYAML-6.0.1-cp310-cp310-win32.whl", hash = "sha256:bd4af7373a854424dabd882decdc5579653d7868b8fb26dc7d0e99f823aa5924"},
     {file = "PyYAML-6.0.1-cp310-cp310-win_amd64.whl", hash = "sha256:fd1592b3fdf65fff2ad0004b5e363300ef59ced41c2e6b3a99d4089fa8c5435d"},
     {file = "PyYAML-6.0.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:6965a7bc3cf88e5a1c3bd2e0b5c22f8d677dc88a455344035f03399034eb3007"},
@@ -3215,8 +3698,15 @@ files = [
     {file = "PyYAML-6.0.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:42f8152b8dbc4fe7d96729ec2b99c7097d656dc1213a3229ca5383f973a5ed6d"},
     {file = "PyYAML-6.0.1-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:062582fca9fabdd2c8b54a3ef1c978d786e0f6b3a1510e0ac93ef59e0ddae2bc"},
     {file = "PyYAML-6.0.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d2b04aac4d386b172d5b9692e2d2da8de7bfb6c387fa4f801fbf6fb2e6ba4673"},
+    {file = "PyYAML-6.0.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:e7d73685e87afe9f3b36c799222440d6cf362062f78be1013661b00c5c6f678b"},
     {file = "PyYAML-6.0.1-cp311-cp311-win32.whl", hash = "sha256:1635fd110e8d85d55237ab316b5b011de701ea0f29d07611174a1b42f1444741"},
     {file = "PyYAML-6.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:bf07ee2fef7014951eeb99f56f39c9bb4af143d8aa3c21b1677805985307da34"},
+    {file = "PyYAML-6.0.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:855fb52b0dc35af121542a76b9a84f8d1cd886ea97c84703eaa6d88e37a2ad28"},
+    {file = "PyYAML-6.0.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:40df9b996c2b73138957fe23a16a4f0ba614f4c0efce1e9406a184b6d07fa3a9"},
+    {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6c22bec3fbe2524cde73d7ada88f6566758a8f7227bfbf93a408a9d86bcc12a0"},
+    {file = "PyYAML-6.0.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8d4e9c88387b0f5c7d5f281e55304de64cf7f9c0021a3525bd3b1c542da3b0e4"},
+    {file = "PyYAML-6.0.1-cp312-cp312-win32.whl", hash = "sha256:d483d2cdf104e7c9fa60c544d92981f12ad66a457afae824d146093b8c294c54"},
+    {file = "PyYAML-6.0.1-cp312-cp312-win_amd64.whl", hash = "sha256:0d3304d8c0adc42be59c5f8a4d9e3d7379e6955ad754aa9d6ab7a398b59dd1df"},
     {file = "PyYAML-6.0.1-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:50550eb667afee136e9a77d6dc71ae76a44df8b3e51e41b77f6de2932bfe0f47"},
     {file = "PyYAML-6.0.1-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1fe35611261b29bd1de0070f0b2f47cb6ff71fa6595c077e42bd0c419fa27b98"},
     {file = "PyYAML-6.0.1-cp36-cp36m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:704219a11b772aea0d8ecd7058d0082713c3562b4e271b849ad7dc4a5c90c13c"},
@@ -3233,6 +3723,7 @@ files = [
     {file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a0cd17c15d3bb3fa06978b4e8958dcdc6e0174ccea823003a106c7d4d7899ac5"},
     {file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:28c119d996beec18c05208a8bd78cbe4007878c6dd15091efb73a30e90539696"},
     {file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7e07cbde391ba96ab58e532ff4803f79c4129397514e1413a7dc761ccd755735"},
+    {file = "PyYAML-6.0.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:49a183be227561de579b4a36efbb21b3eab9651dd81b1858589f796549873dd6"},
     {file = "PyYAML-6.0.1-cp38-cp38-win32.whl", hash = "sha256:184c5108a2aca3c5b3d3bf9395d50893a7ab82a38004c8f61c258d4428e80206"},
     {file = "PyYAML-6.0.1-cp38-cp38-win_amd64.whl", hash = "sha256:1e2722cc9fbb45d9b87631ac70924c11d3a401b2d7f410cc0e3bbf249f2dca62"},
     {file = "PyYAML-6.0.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:9eb6caa9a297fc2c2fb8862bc5370d0303ddba53ba97e71f08023b6cd73d16a8"},
@@ -3240,6 +3731,7 @@ files = [
     {file = "PyYAML-6.0.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5773183b6446b2c99bb77e77595dd486303b4faab2b086e7b17bc6bef28865f6"},
     {file = "PyYAML-6.0.1-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b786eecbdf8499b9ca1d697215862083bd6d2a99965554781d0d8d1ad31e13a0"},
     {file = "PyYAML-6.0.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bc1bf2925a1ecd43da378f4db9e4f799775d6367bdb94671027b73b393a7c42c"},
+    {file = "PyYAML-6.0.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:04ac92ad1925b2cff1db0cfebffb6ffc43457495c9b3c39d3fcae417d7125dc5"},
     {file = "PyYAML-6.0.1-cp39-cp39-win32.whl", hash = "sha256:faca3bdcf85b2fc05d06ff3fbc1f83e1391b3e724afa3feba7d13eeab355484c"},
     {file = "PyYAML-6.0.1-cp39-cp39-win_amd64.whl", hash = "sha256:510c9deebc5c0225e8c96813043e62b680ba2f9c50a08d3724c7f28a747d1486"},
     {file = "PyYAML-6.0.1.tar.gz", hash = "sha256:bfdf460b1736c775f2ba9f6a92bca30bc2095067b8a9d77876d1fad6cc3b4a43"},
@@ -5016,4 +5508,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8,<4.0"
-content-hash = "2c1024e2a543acbbc1366658aad02c355a1eae60d26b5281664dd22465942ff2"
+content-hash = "d0c953a0bc891c3e9a7988486072649d6303c9d6aa3e02f8019928a9b2e7f04e"
diff --git a/pyproject.toml b/pyproject.toml
index 16cc9b045..270ab5859 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -66,6 +66,7 @@ nbval = "^0.10.0"
 
 [tool.poetry.group.jupyter.dependencies]
 jupyterlab = ">=3.5.0"
+matplotlib = "3.7"
 
 [tool.poetry.group.docs.dependencies]
 sphinx = {version = "5.2.3" }

From 2a551623da3c1bf57b86f768ae46f3cd36a77802 Mon Sep 17 00:00:00 2001
From: Bryce Meyer <bryce13950@gmail.com>
Date: Mon, 23 Oct 2023 13:57:45 +0200
Subject: [PATCH 06/25] updated circuitsvis

---
 poetry.lock | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index c5db5081f..2d7f3bb39 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -641,13 +641,13 @@ files = [
 
 [[package]]
 name = "circuitsvis"
-version = "1.42.0"
+version = "1.43.0"
 description = "Mechanistic Interpretability Visualizations"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "circuitsvis-1.42.0-py3-none-any.whl", hash = "sha256:0f39c513c844d6291d5a12476e17b172c9de6918129b17ae7b72831d9df817b6"},
-    {file = "circuitsvis-1.42.0.tar.gz", hash = "sha256:8fe2d9269dd228d67a401b212425612b1b002fc3eb92356a1838c1421792f879"},
+    {file = "circuitsvis-1.43.0-py3-none-any.whl", hash = "sha256:f680419d9a2c26cb94ee8311ace007214e09b236396be0185e6e45c6b4ae0e3a"},
+    {file = "circuitsvis-1.43.0.tar.gz", hash = "sha256:b4dfd1506b0acc13b859eafbb0a987e7625db6b8c87fd51a4c9254990cff0d58"},
 ]
 
 [package.dependencies]

From 90b4e155206d14b25d48baa42b11746da6ed05b6 Mon Sep 17 00:00:00 2001
From: Bryce Meyer <bryce13950@gmail.com>
Date: Mon, 23 Oct 2023 14:24:02 +0200
Subject: [PATCH 07/25] added sanitize file

---
 demos/Main_Demo.ipynb  | 14 +++++++++-----
 demos/doc_sanitize.cfg | 11 +++++++++++
 makefile               |  4 ++--
 3 files changed, 22 insertions(+), 7 deletions(-)
 create mode 100644 demos/doc_sanitize.cfg

diff --git a/demos/Main_Demo.ipynb b/demos/Main_Demo.ipynb
index fd9c73303..ecb2b5232 100644
--- a/demos/Main_Demo.ipynb
+++ b/demos/Main_Demo.ipynb
@@ -107,22 +107,26 @@
                 {
                     "data": {
                         "text/html": [
-                            "<div id=\"circuits-vis-84d5f322-d9bc\" style=\"margin: 15px 0;\"/>\n",
+                            "<div id=\"circuits-vis-47ed0393-c2b8\" style=\"margin: 15px 0;\"/>\n",
                             "    <script crossorigin type=\"module\">\n",
-                            "    import { render, Hello } from \"https://unpkg.com/circuitsvis@1.42.0/dist/cdn/esm.js\";\n",
+                            "    import { render, Hello } from \"https://unpkg.com/circuitsvis@1.43.0/dist/cdn/esm.js\";\n",
                             "    render(\n",
-                            "      \"circuits-vis-84d5f322-d9bc\",\n",
+                            "      \"circuits-vis-47ed0393-c2b8\",\n",
                             "      Hello,\n",
                             "      {\"name\": \"Neel\"}\n",
                             "    )\n",
                             "    </script>"
                         ],
                         "text/plain": [
-                            "<circuitsvis.utils.render.RenderedHTML at 0xffffac0c6c70>"
+                            "<circuitsvis.utils.render.RenderedHTML at 0xffff8816cbb0>"
                         ]
                     },
                     "execution_count": 3,
-                    "metadata": {},
+                    "metadata": {
+                        "text/html": {
+                            "Content-Type": "text/html"
+                        }
+                    },
                     "output_type": "execute_result"
                 }
             ],
diff --git a/demos/doc_sanitize.cfg b/demos/doc_sanitize.cfg
new file mode 100644
index 000000000..c21ea942f
--- /dev/null
+++ b/demos/doc_sanitize.cfg
@@ -0,0 +1,11 @@
+[regex1]
+regex: \d{1,2}/\d{1,2}/\d{2,4}
+replace: DATE-STAMP
+
+[regex2]
+regex: \d{2}:\d{2}:\d{2}
+replace: TIME-STAMP
+
+[regex3]
+regex: 0[xX][0-9a-fA-F]+
+replace: HEX-CODE
\ No newline at end of file
diff --git a/makefile b/makefile
index 08f2fb9b8..96f4fa0d1 100644
--- a/makefile
+++ b/makefile
@@ -18,8 +18,8 @@ docstring-test:
 	poetry run pytest transformer_lens/
 
 notebook-test:
-	poetry run pytest demos/Main_Demo.ipynb 
-	poetry run pytest demos/Exploratory_Analysis_Demo.ipynb
+	poetry run pytest --sanitize-with demos/doc_sanitize.cfg demos/Main_Demo.ipynb 
+	poetry run pytest --sanitize-with demos/doc_sanitize.cfg demos/Exploratory_Analysis_Demo.ipynb
 
 test:
 	make unit-test

From 52f53b28640981e46a0773de68c0c4d5e4c77e80 Mon Sep 17 00:00:00 2001
From: Bryce Meyer <bryce13950@gmail.com>
Date: Mon, 23 Oct 2023 14:51:04 +0200
Subject: [PATCH 08/25] regenerated output

---
 demos/Main_Demo.ipynb | 247 ++++++++++++++++++++++++------------------
 1 file changed, 140 insertions(+), 107 deletions(-)

diff --git a/demos/Main_Demo.ipynb b/demos/Main_Demo.ipynb
index ecb2b5232..22b092569 100644
--- a/demos/Main_Demo.ipynb
+++ b/demos/Main_Demo.ipynb
@@ -45,7 +45,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 1,
+            "execution_count": 121,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -78,7 +78,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 2,
+            "execution_count": 122,
             "metadata": {},
             "outputs": [
                 {
@@ -101,27 +101,27 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 3,
+            "execution_count": 123,
             "metadata": {},
             "outputs": [
                 {
                     "data": {
                         "text/html": [
-                            "<div id=\"circuits-vis-47ed0393-c2b8\" style=\"margin: 15px 0;\"/>\n",
+                            "<div id=\"circuits-vis-1f1775a5-9164\" style=\"margin: 15px 0;\"/>\n",
                             "    <script crossorigin type=\"module\">\n",
                             "    import { render, Hello } from \"https://unpkg.com/circuitsvis@1.43.0/dist/cdn/esm.js\";\n",
                             "    render(\n",
-                            "      \"circuits-vis-47ed0393-c2b8\",\n",
+                            "      \"circuits-vis-1f1775a5-9164\",\n",
                             "      Hello,\n",
                             "      {\"name\": \"Neel\"}\n",
                             "    )\n",
                             "    </script>"
                         ],
                         "text/plain": [
-                            "<circuitsvis.utils.render.RenderedHTML at 0xffff8816cbb0>"
+                            "<circuitsvis.utils.render.RenderedHTML at 0xffff4bbf8f70>"
                         ]
                     },
-                    "execution_count": 3,
+                    "execution_count": 123,
                     "metadata": {
                         "text/html": {
                             "Content-Type": "text/html"
@@ -138,7 +138,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 4,
+            "execution_count": 124,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -170,7 +170,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 5,
+            "execution_count": 125,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -192,16 +192,16 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 6,
+            "execution_count": 126,
             "metadata": {},
             "outputs": [
                 {
                     "data": {
                         "text/plain": [
-                            "<torch.autograd.grad_mode.set_grad_enabled at 0xffff5fdcab20>"
+                            "<torch.autograd.grad_mode.set_grad_enabled at 0xffff4a625b80>"
                         ]
                     },
-                    "execution_count": 6,
+                    "execution_count": 126,
                     "metadata": {},
                     "output_type": "execute_result"
                 }
@@ -219,7 +219,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 7,
+            "execution_count": 127,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -267,7 +267,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 8,
+            "execution_count": 128,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -276,7 +276,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 9,
+            "execution_count": 129,
             "metadata": {},
             "outputs": [
                 {
@@ -312,7 +312,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 10,
+            "execution_count": 130,
             "metadata": {},
             "outputs": [
                 {
@@ -350,7 +350,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 11,
+            "execution_count": 131,
             "metadata": {},
             "outputs": [
                 {
@@ -384,7 +384,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 12,
+            "execution_count": 132,
             "metadata": {},
             "outputs": [
                 {
@@ -405,7 +405,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 13,
+            "execution_count": 133,
             "metadata": {},
             "outputs": [
                 {
@@ -418,22 +418,26 @@
                 {
                     "data": {
                         "text/html": [
-                            "<div id=\"circuits-vis-01fa67fc-f7e6\" style=\"margin: 15px 0;\"/>\n",
+                            "<div id=\"circuits-vis-5be09bfe-07c2\" style=\"margin: 15px 0;\"/>\n",
                             "    <script crossorigin type=\"module\">\n",
-                            "    import { render, AttentionPatterns } from \"https://unpkg.com/circuitsvis@1.42.0/dist/cdn/esm.js\";\n",
+                            "    import { render, AttentionPatterns } from \"https://unpkg.com/circuitsvis@1.43.0/dist/cdn/esm.js\";\n",
                             "    render(\n",
-                            "      \"circuits-vis-01fa67fc-f7e6\",\n",
+                            "      \"circuits-vis-5be09bfe-07c2\",\n",
                             "      AttentionPatterns,\n",
                             "      {\"tokens\": [\"<|endoftext|>\", \"Natural\", \" language\", \" processing\", \" tasks\", \",\", \" such\", \" as\", \" question\", \" answering\", \",\", \" machine\", \" translation\", \",\", \" reading\", \" comprehension\", \",\", \" and\", \" summar\", \"ization\", \",\", \" are\", \" typically\", \" approached\", \" with\", \" supervised\", \" learning\", \" on\", \" tasks\", \"pe\", \"cific\", \" datasets\", \".\"], \"attention\": [[[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.9639418125152588, 0.03605816140770912, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.8389372825622559, 0.11828784644603729, 0.042774833738803864, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 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[0.1521887481212616, 0.032320477068424225, 0.02627762220799923, 0.032151371240615845, 0.0370805524289608, 0.03843334689736366, 0.042185209691524506, 0.03440922126173973, 0.02929617278277874, 0.024752285331487656, 0.034612320363521576, 0.021795086562633514, 0.023419689387083054, 0.03386644273996353, 0.028580596670508385, 0.034403245896101, 0.03315696492791176, 0.0267103910446167, 0.04213566333055496, 0.01902136206626892, 0.0327858068048954, 0.02683998830616474, 0.03304454684257507, 0.02330263890326023, 0.03390035405755043, 0.03944927453994751, 0.021334253251552582, 0.042546432465314865, 0.0, 0.0, 0.0, 0.0, 0.0], [0.10210394114255905, 0.048305343836545944, 0.027957916259765625, 0.028134865686297417, 0.04368893429636955, 0.03765961155295372, 0.040121808648109436, 0.03589581698179245, 0.024491779506206512, 0.01976805366575718, 0.03467409312725067, 0.01543145440518856, 0.032616566866636276, 0.03414338454604149, 0.027461379766464233, 0.02032320387661457, 0.03445841372013092, 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0.014118616469204426, 0.013909779489040375, 0.05736804008483887, 0.0345463864505291, 0.04665059596300125, 0.013026435859501362, 0.013271292671561241, 0.05952776223421097, 0.009063143283128738, 0.01736420951783657, 0.06135563179850578, 0.012634643353521824, 0.015145537443459034, 0.06425533443689346, 0.04627062380313873, 0.01955636590719223, 0.012543467804789543, 0.06913556158542633, 0.034823667258024216, 0.03000595234334469, 0.01226181723177433, 0.07985590398311615, 0.01899644173681736, 0.010722930543124676, 0.086545430123806, 0.017174087464809418, 0.02214415930211544, 0.022453967481851578, 0.0, 0.0], [0.05849643424153328, 0.03437737748026848, 0.0136030912399292, 0.024170435965061188, 0.019005853682756424, 0.06546405702829361, 0.03062296472489834, 0.03573206812143326, 0.018859446048736572, 0.012241474352777004, 0.0628218874335289, 0.011835743673145771, 0.009552356787025928, 0.06431794911623001, 0.02158956602215767, 0.010912960395216942, 0.06665913015604019, 0.023997457697987556, 0.026596754789352417, 0.006153552792966366, 0.06984327733516693, 0.037238702178001404, 0.03004671260714531, 0.02168680727481842, 0.043640609830617905, 0.02120964229106903, 0.013490465469658375, 0.046327244490385056, 0.02043450064957142, 0.01336225401610136, 0.018568992614746094, 0.047140248119831085, 0.0], [0.1352868378162384, 0.02973336912691593, 0.026111334562301636, 0.03753575310111046, 0.0388815738260746, 0.0331667996942997, 0.04824388772249222, 0.030468309298157692, 0.02603820152580738, 0.024123704060912132, 0.02789273113012314, 0.022709932178258896, 0.024185284972190857, 0.02611454762518406, 0.02320965938270092, 0.025175275281071663, 0.02461479790508747, 0.021123144775629044, 0.02968505397439003, 0.018167467787861824, 0.023064393550157547, 0.02059808000922203, 0.03441140428185463, 0.021083392202854156, 0.02253883145749569, 0.032281555235385895, 0.013987245969474316, 0.024144919589161873, 0.027002302929759026, 0.012929439544677734, 0.03616538643836975, 0.02684023417532444, 0.03248501941561699]]]}\n",
                             "    )\n",
                             "    </script>"
                         ],
                         "text/plain": [
-                            "<circuitsvis.utils.render.RenderedHTML at 0xffff6e86ae20>"
+                            "<circuitsvis.utils.render.RenderedHTML at 0xffff4b46fe20>"
                         ]
                     },
-                    "execution_count": 13,
-                    "metadata": {},
+                    "execution_count": 133,
+                    "metadata": {
+                        "text/html": {
+                            "Content-Type": "text/html"
+                        }
+                    },
                     "output_type": "execute_result"
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             ],
@@ -482,7 +486,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 14,
+            "execution_count": 134,
             "metadata": {},
             "outputs": [
                 {
@@ -561,7 +565,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 15,
+            "execution_count": 135,
             "metadata": {},
             "outputs": [
                 {
@@ -610,13 +614,13 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 16,
+            "execution_count": 136,
             "metadata": {},
             "outputs": [
                 {
                     "data": {
                         "application/vnd.jupyter.widget-view+json": {
-                            "model_id": "267fc32b322044f8896d7ac5b935ea5d",
+                            "model_id": "be4c830c189244929fdd66bbb9907e32",
                             "version_major": 2,
                             "version_minor": 0
                         },
@@ -670,24 +674,47 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 17,
+            "execution_count": null,
             "metadata": {},
             "outputs": [
                 {
-                    "ename": "ModuleNotFoundError",
-                    "evalue": "No module named 'matplotlib'",
-                    "output_type": "error",
-                    "traceback": [
-                        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-                        "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
-                        "\u001b[1;32m/workspaces/TransformerLens/demos/Main_Demo.ipynb Cell 38\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> <a href='vscode-notebook-cell://dev-container%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_Demo.ipynb#X52sdnNjb2RlLXJlbW90ZQ%3D%3D?line=0'>1</a>\u001b[0m get_ipython()\u001b[39m.\u001b[39;49mrun_line_magic(\u001b[39m'\u001b[39;49m\u001b[39mmatplotlib\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39m'\u001b[39;49m\u001b[39minline\u001b[39;49m\u001b[39m'\u001b[39;49m)\n\u001b[1;32m      <a href='vscode-notebook-cell://dev-container%2B7b22686f737450617468223a222f55736572732f62727963652f50726f6a656374732f4c696e67776176652f5472616e73666f726d65724c656e73222c226c6f63616c446f636b6572223a66616c73652c2273657474696e6773223a7b22636f6e74657874223a226465736b746f702d6c696e7578227d2c22636f6e66696746696c65223a7b22246d6964223a312c22667350617468223a222f55736572732f62727963652f50726f6a656374732f4c696e67776176652f5472616e73666f726d65724c656e732f2e646576636f6e7461696e65722f646576636f6e7461696e65722e6a736f6e222c2265787465726e616c223a2266696c653a2f2f2f55736572732f62727963652f50726f6a656374732f4c696e67776176652f5472616e73666f726d65724c656e732f2e646576636f6e7461696e65722f646576636f6e7461696e65722e6a736f6e222c2270617468223a222f55736572732f62727963652f50726f6a656374732f4c696e67776176652f5472616e73666f726d65724c656e732f2e646576636f6e7461696e65722f646576636f6e7461696e65722e6a736f6e222c22736368656d65223a2266696c65227d7d/workspaces/TransformerLens/demos/Main_Demo.ipynb#X52sdnNjb2RlLXJlbW90ZQ%3D%3D?line=1'>2</a>\u001b[0m \u001b[39m# Add the index to the end of the label, because plotly doesn't like duplicate labels\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell://dev-container%2B7b22686f737450617468223a222f55736572732f62727963652f50726f6a656374732f4c696e67776176652f5472616e73666f726d65724c656e73222c226c6f63616c446f636b6572223a66616c73652c2273657474696e6773223a7b22636f6e74657874223a226465736b746f702d6c696e7578227d2c22636f6e66696746696c65223a7b22246d6964223a312c22667350617468223a222f55736572732f62727963652f50726f6a656374732f4c696e67776176652f5472616e73666f726d65724c656e732f2e646576636f6e7461696e65722f646576636f6e7461696e65722e6a736f6e222c2265787465726e616c223a2266696c653a2f2f2f55736572732f62727963652f50726f6a656374732f4c696e67776176652f5472616e73666f726d65724c656e732f2e646576636f6e7461696e65722f646576636f6e7461696e65722e6a736f6e222c2270617468223a222f55736572732f62727963652f50726f6a656374732f4c696e67776176652f5472616e73666f726d65724c656e732f2e646576636f6e7461696e65722f646576636f6e7461696e65722e6a736f6e222c22736368656d65223a2266696c65227d7d/workspaces/TransformerLens/demos/Main_Demo.ipynb#X52sdnNjb2RlLXJlbW90ZQ%3D%3D?line=2'>3</a>\u001b[0m token_labels \u001b[39m=\u001b[39m [\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00mtoken\u001b[39m}\u001b[39;00m\u001b[39m_\u001b[39m\u001b[39m{\u001b[39;00mindex\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m \u001b[39mfor\u001b[39;00m index, token \u001b[39min\u001b[39;00m \u001b[39menumerate\u001b[39m(model\u001b[39m.\u001b[39mto_str_tokens(clean_tokens))]\n",
-                        "File \u001b[0;32m/workspaces/TransformerLens/.venv/lib/python3.8/site-packages/IPython/core/interactiveshell.py:2417\u001b[0m, in \u001b[0;36mInteractiveShell.run_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m   2415\u001b[0m     kwargs[\u001b[39m'\u001b[39m\u001b[39mlocal_ns\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mget_local_scope(stack_depth)\n\u001b[1;32m   2416\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mbuiltin_trap:\n\u001b[0;32m-> 2417\u001b[0m     result \u001b[39m=\u001b[39m fn(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   2419\u001b[0m \u001b[39m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m   2420\u001b[0m \u001b[39m# when using magics with decodator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m   2421\u001b[0m \u001b[39m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m   2422\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mgetattr\u001b[39m(fn, magic\u001b[39m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[39mFalse\u001b[39;00m):\n",
-                        "File \u001b[0;32m/workspaces/TransformerLens/.venv/lib/python3.8/site-packages/IPython/core/magics/pylab.py:99\u001b[0m, in \u001b[0;36mPylabMagics.matplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m     97\u001b[0m     \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mAvailable matplotlib backends: \u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m\"\u001b[39m \u001b[39m%\u001b[39m backends_list)\n\u001b[1;32m     98\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m---> 99\u001b[0m     gui, backend \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mshell\u001b[39m.\u001b[39;49menable_matplotlib(args\u001b[39m.\u001b[39;49mgui\u001b[39m.\u001b[39;49mlower() \u001b[39mif\u001b[39;49;00m \u001b[39misinstance\u001b[39;49m(args\u001b[39m.\u001b[39;49mgui, \u001b[39mstr\u001b[39;49m) \u001b[39melse\u001b[39;49;00m args\u001b[39m.\u001b[39;49mgui)\n\u001b[1;32m    100\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_show_matplotlib_backend(args\u001b[39m.\u001b[39mgui, backend)\n",
-                        "File \u001b[0;32m/workspaces/TransformerLens/.venv/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3588\u001b[0m, in \u001b[0;36mInteractiveShell.enable_matplotlib\u001b[0;34m(self, gui)\u001b[0m\n\u001b[1;32m   3567\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39menable_matplotlib\u001b[39m(\u001b[39mself\u001b[39m, gui\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m):\n\u001b[1;32m   3568\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"Enable interactive matplotlib and inline figure support.\u001b[39;00m\n\u001b[1;32m   3569\u001b[0m \n\u001b[1;32m   3570\u001b[0m \u001b[39m    This takes the following steps:\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   3586\u001b[0m \u001b[39m        display figures inline.\u001b[39;00m\n\u001b[1;32m   3587\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3588\u001b[0m     \u001b[39mfrom\u001b[39;00m \u001b[39mmatplotlib_inline\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mbackend_inline\u001b[39;00m \u001b[39mimport\u001b[39;00m configure_inline_support\n\u001b[1;32m   3590\u001b[0m     \u001b[39mfrom\u001b[39;00m \u001b[39mIPython\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mcore\u001b[39;00m \u001b[39mimport\u001b[39;00m pylabtools \u001b[39mas\u001b[39;00m pt\n\u001b[1;32m   3591\u001b[0m     gui, backend \u001b[39m=\u001b[39m pt\u001b[39m.\u001b[39mfind_gui_and_backend(gui, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpylab_gui_select)\n",
-                        "File \u001b[0;32m/workspaces/TransformerLens/.venv/lib/python3.8/site-packages/matplotlib_inline/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m \u001b[39mimport\u001b[39;00m backend_inline, config  \u001b[39m# noqa\u001b[39;00m\n\u001b[1;32m      2\u001b[0m __version__ \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m0.1.6\u001b[39m\u001b[39m\"\u001b[39m  \u001b[39m# noqa\u001b[39;00m\n",
-                        "File \u001b[0;32m/workspaces/TransformerLens/.venv/lib/python3.8/site-packages/matplotlib_inline/backend_inline.py:6\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[39m\"\"\"A matplotlib backend for publishing figures via display_data\"\"\"\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[39m# Copyright (c) IPython Development Team.\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \u001b[39m# Distributed under the terms of the BSD 3-Clause License.\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mmatplotlib\u001b[39;00m\n\u001b[1;32m      7\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mmatplotlib\u001b[39;00m \u001b[39mimport\u001b[39;00m colors\n\u001b[1;32m      8\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mmatplotlib\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mbackends\u001b[39;00m \u001b[39mimport\u001b[39;00m backend_agg\n",
-                        "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'"
-                    ]
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                             "                            \n",
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@@ -893,34 +920,28 @@
             "execution_count": null,
             "metadata": {},
             "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
-                        "To disable this warning, you can either:\n",
-                        "\t- Avoid using `tokenizers` before the fork if possible\n",
-                        "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
-                    ]
-                },
                 {
                     "data": {
                         "text/html": [
-                            "<div id=\"circuits-vis-a5255d3f-e6b3\" style=\"margin: 15px 0;\"/>\n",
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                             "    <script crossorigin type=\"module\">\n",
-                            "    import { render, AttentionPatterns } from \"https://unpkg.com/circuitsvis/dist/cdn/esm.js\";\n",
+                            "    import { render, AttentionPatterns } from \"https://unpkg.com/circuitsvis@1.43.0/dist/cdn/esm.js\";\n",
                             "    render(\n",
-                            "      \"circuits-vis-a5255d3f-e6b3\",\n",
+                            "      \"circuits-vis-6b968f5e-2340\",\n",
                             "      AttentionPatterns,\n",
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                             "                            \n",
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@@ -1264,12 +1285,16 @@
                     "name": "stdout",
                     "output_type": "stream",
                     "text": [
-                        "Num tokens: 1\n",
+                        "Num tokens: 10\n",
                         "hook_embed torch.Size([1, 10, 768])\n",
                         "hook_pos_embed torch.Size([1, 10, 768])\n",
                         "blocks.0.hook_resid_pre torch.Size([1, 10, 768])\n",
                         "blocks.0.ln1.hook_scale torch.Size([1, 10, 1])\n",
                         "blocks.0.ln1.hook_normalized torch.Size([1, 10, 768])\n",
+                        "blocks.0.ln1.hook_scale torch.Size([1, 10, 1])\n",
+                        "blocks.0.ln1.hook_normalized torch.Size([1, 10, 768])\n",
+                        "blocks.0.ln1.hook_scale torch.Size([1, 10, 1])\n",
+                        "blocks.0.ln1.hook_normalized torch.Size([1, 10, 768])\n",
                         "blocks.0.attn.hook_q torch.Size([1, 10, 12, 64])\n",
                         "blocks.0.attn.hook_k torch.Size([1, 10, 12, 64])\n",
                         "blocks.0.attn.hook_v torch.Size([1, 10, 12, 64])\n",
@@ -1527,7 +1552,7 @@
                         "           257,  4731,   656,   262, 16326,  1635,   292,   257,  1351,   286,\n",
                         "           850, 37336, 25666,   290,   523,  8781,   345,  7301,   644,   262,\n",
                         "          2420,  3073,   588,    13,  1675, 10176,   428,    11,  1309,   338,\n",
-                        "           779,   340,   319,   428,  7322,    13]], device='cuda:0')\n"
+                        "           779,   340,   319,   428,  7322,    13]])\n"
                     ]
                 }
             ],
@@ -1555,8 +1580,7 @@
                     "output_type": "stream",
                     "text": [
                         "tensor([[50256,   464,  3797,  3332,   319,   262,  2603,    13, 50256, 50256],\n",
-                        "        [50256,   464,  3797,  3332,   319,   262,  2603,  1107,  1327,    13]],\n",
-                        "       device='cuda:0')\n"
+                        "        [50256,   464,  3797,  3332,   319,   262,  2603,  1107,  1327,    13]])\n"
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@@ -1878,8 +1902,8 @@
                     "output_type": "stream",
                     "text": [
                         "Norms:\n",
-                        "tensor(5.7439)\n",
-                        "tensor(5.7439)\n",
+                        "tensor(9.2448)\n",
+                        "tensor(9.2448)\n",
                         "Right dimension: 5, Left dimension: 5, Hidden dimension: 2\n"
                     ]
                 }
@@ -1913,14 +1937,13 @@
                     "output_type": "stream",
                     "text": [
                         "Eigenvalues:\n",
-                        "tensor([-1.6199e+00+0.0000e+00j,  1.0446e+00+0.0000e+00j,\n",
-                        "         3.7190e-08+0.0000e+00j, -6.5998e-08+1.2859e-07j,\n",
-                        "        -6.5998e-08-1.2859e-07j])\n",
-                        "tensor([-1.6199+0.j,  1.0446+0.j])\n",
+                        "tensor([-2.9891e+00+0.j,  9.8913e-09+0.j, -4.8101e-01+0.j,  1.7593e-07+0.j,\n",
+                        "        -4.8844e-08+0.j])\n",
+                        "tensor([-2.9891+0.j, -0.4810+0.j])\n",
                         "\n",
                         "Singular Values:\n",
-                        "tensor([5.4702e+00, 1.7519e+00, 3.4613e-07, 1.0601e-07, 3.8823e-09])\n",
-                        "tensor([5.4702, 1.7519])\n"
+                        "tensor([8.9167e+00, 2.4411e+00, 4.6067e-07, 6.3512e-08, 3.8117e-09])\n",
+                        "tensor([8.9167, 2.4411])\n"
                     ]
                 }
             ],
@@ -1950,8 +1973,8 @@
                     "name": "stdout",
                     "output_type": "stream",
                     "text": [
-                        "Unfactored: torch.Size([5, 300]) tensor(99.9906)\n",
-                        "Factored: torch.Size([5, 300]) tensor(99.9906)\n",
+                        "Unfactored: torch.Size([5, 300]) tensor(153.5882)\n",
+                        "Factored: torch.Size([5, 300]) tensor(153.5882)\n",
                         "Right dimension: 300, Left dimension: 5, Hidden dimension: 2\n"
                     ]
                 }
@@ -2065,9 +2088,9 @@
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                         "text/plain": [
-                            "'(CNN) President Barack Obama caught in embarrassing new scandal\\n\\nCNN reported Wednesday that the former president was charged with lying to the FBI about his relationship with a Russian employee.\\n\\nThe FBI said in a statement that it had \"conducted the investigation\" into the matter.\\n\\nObama has pleaded not'"
+                            "'(CNN) President Barack Obama caught in embarrassing new scandal\\n\\nThe president had an affair with a young woman\\n\\nThe White House press secretary said her comments were \"disgusting\" and that they were \"disgusting\" because she denied the allegations\\n\\n\"I have to say, I think'"
                         ]
                     },
-                    "execution_count": 52,
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Values for solu-6l-pile (Linear scale)\"}},                        {\"responsive\": true}                    ).then(function(){\n",
                             "                            \n",
-                            "var gd = document.getElementById('440d9dd4-5051-445a-be32-9ad92e71921a');\n",
+                            "var gd = document.getElementById('a9d47cb2-6d92-4cec-a2a6-066f57a4e496');\n",
                             "var x = new MutationObserver(function (mutations, observer) {{\n",
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@@ -2857,24 +2880,34 @@
                     "name": "stdout",
                     "output_type": "stream",
                     "text": [
-                        "Loaded pretrained model solu-2l into HookedTransformer\n",
-                        "Loaded pretrained model solu-2l into HookedTransformer\n",
-                        "Loaded pretrained model solu-2l into HookedTransformer\n",
-                        "Loaded pretrained model solu-2l into HookedTransformer\n",
                         "Loaded pretrained model solu-2l into HookedTransformer\n"
                     ]
+                },
+                {
+                    "ename": "AssertionError",
+                    "evalue": "Torch not compiled with CUDA enabled",
+                    "output_type": "error",
+                    "traceback": [
+                        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+                        "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+                        "\u001b[1;32m/workspaces/TransformerLens/demos/Main_Demo.ipynb Cell 142\u001b[0m line \u001b[0;36m9\n\u001b[1;32m      <a href='vscode-notebook-cell://dev-container%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_Demo.ipynb#Y261sdnNjb2RlLXJlbW90ZQ%3D%3D?line=5'>6</a>\u001b[0m tokens_seen_for_this_checkpoint \u001b[39m=\u001b[39m model_for_this_checkpoint\u001b[39m.\u001b[39mcfg\u001b[39m.\u001b[39mcheckpoint_value\n\u001b[1;32m      <a href='vscode-notebook-cell://dev-container%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_Demo.ipynb#Y261sdnNjb2RlLXJlbW90ZQ%3D%3D?line=6'>7</a>\u001b[0m tokens_trained_on\u001b[39m.\u001b[39mappend(tokens_seen_for_this_checkpoint)\n\u001b[0;32m----> <a href='vscode-notebook-cell://dev-container%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_Demo.ipynb#Y261sdnNjb2RlLXJlbW90ZQ%3D%3D?line=8'>9</a>\u001b[0m induction_loss_for_this_checkpoint \u001b[39m=\u001b[39m evals\u001b[39m.\u001b[39;49minduction_loss(model_for_this_checkpoint)\u001b[39m.\u001b[39mitem()\n\u001b[1;32m     <a href='vscode-notebook-cell://dev-container%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_Demo.ipynb#Y261sdnNjb2RlLXJlbW90ZQ%3D%3D?line=9'>10</a>\u001b[0m induction_losses\u001b[39m.\u001b[39mappend(induction_loss_for_this_checkpoint)\n",
+                        "File \u001b[0;32m/workspaces/TransformerLens/.venv/lib/python3.8/site-packages/torch/utils/_contextlib.py:115\u001b[0m, in \u001b[0;36mcontext_decorator.<locals>.decorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    112\u001b[0m \u001b[39m@functools\u001b[39m\u001b[39m.\u001b[39mwraps(func)\n\u001b[1;32m    113\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mdecorate_context\u001b[39m(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m    114\u001b[0m     \u001b[39mwith\u001b[39;00m ctx_factory():\n\u001b[0;32m--> 115\u001b[0m         \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
+                        "File \u001b[0;32m/workspaces/TransformerLens/transformer_lens/evals.py:133\u001b[0m, in \u001b[0;36minduction_loss\u001b[0;34m(model, tokenizer, batch_size, subseq_len, prepend_bos, device)\u001b[0m\n\u001b[1;32m    126\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    127\u001b[0m \u001b[39mGenerates a batch of random sequences repeated twice, and measures model performance on the second half. Tests whether a model has induction heads.\u001b[39;00m\n\u001b[1;32m    128\u001b[0m \n\u001b[1;32m    129\u001b[0m \u001b[39mBy default, prepends a beginning of string token (when prepend_bos flag defaults to None, model.cfg.default_prepend_bos is used\u001b[39;00m\n\u001b[1;32m    130\u001b[0m \u001b[39mwhose default is True unless specified otherwise), which is useful to give models a resting position, and sometimes models were trained with this.\u001b[39;00m\n\u001b[1;32m    131\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    132\u001b[0m \u001b[39m# Make the repeated sequence\u001b[39;00m\n\u001b[0;32m--> 133\u001b[0m first_half_tokens \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39;49mrandint(\u001b[39m100\u001b[39;49m, \u001b[39m20000\u001b[39;49m, (batch_size, subseq_len))\u001b[39m.\u001b[39;49mto(device)\n\u001b[1;32m    134\u001b[0m repeated_tokens \u001b[39m=\u001b[39m einops\u001b[39m.\u001b[39mrepeat(first_half_tokens, \u001b[39m\"\u001b[39m\u001b[39mb p -> b (2 p)\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m    136\u001b[0m \u001b[39m# Use the provided prepend_bos as an override if it's not None;\u001b[39;00m\n\u001b[1;32m    137\u001b[0m \u001b[39m# otherwise use model.cfg.default_prepend_bos (defaults to True)\u001b[39;00m\n",
+                        "File \u001b[0;32m/workspaces/TransformerLens/.venv/lib/python3.8/site-packages/torch/cuda/__init__.py:289\u001b[0m, in \u001b[0;36m_lazy_init\u001b[0;34m()\u001b[0m\n\u001b[1;32m    284\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mRuntimeError\u001b[39;00m(\n\u001b[1;32m    285\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mCannot re-initialize CUDA in forked subprocess. To use CUDA with \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    286\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mmultiprocessing, you must use the \u001b[39m\u001b[39m'\u001b[39m\u001b[39mspawn\u001b[39m\u001b[39m'\u001b[39m\u001b[39m start method\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    287\u001b[0m     )\n\u001b[1;32m    288\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mhasattr\u001b[39m(torch\u001b[39m.\u001b[39m_C, \u001b[39m\"\u001b[39m\u001b[39m_cuda_getDeviceCount\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[0;32m--> 289\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mAssertionError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mTorch not compiled with CUDA enabled\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m    290\u001b[0m \u001b[39mif\u001b[39;00m _cudart \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    291\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mAssertionError\u001b[39;00m(\n\u001b[1;32m    292\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mlibcudart functions unavailable. It looks like you have a broken build?\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    293\u001b[0m     )\n",
+                        "\u001b[0;31mAssertionError\u001b[0m: Torch not compiled with CUDA enabled"
+                    ]
                 }
             ],
             "source": [
                 "for index in checkpoint_indices:\n",
                 "    # Load the model from the relevant checkpoint by index\n",
-                "    model_for_this_checkpoint = HookedTransformer.from_pretrained(model_name, checkpoint_index=index)\n",
+                "    model_for_this_checkpoint = HookedTransformer.from_pretrained(model_name, checkpoint_index=index, device=device)\n",
                 "    checkpointed_models.append(model_for_this_checkpoint)\n",
                 "\n",
                 "    tokens_seen_for_this_checkpoint = model_for_this_checkpoint.cfg.checkpoint_value\n",
                 "    tokens_trained_on.append(tokens_seen_for_this_checkpoint)\n",
                 "\n",
-                "    induction_loss_for_this_checkpoint = evals.induction_loss(model_for_this_checkpoint).item()\n",
+                "    induction_loss_for_this_checkpoint = evals.induction_loss(model_for_this_checkpoint, device=device).item()\n",
                 "    induction_losses.append(induction_loss_for_this_checkpoint)"
             ]
         },

From 1bd389591a7685a840cb82dd6cce753daff26d48 Mon Sep 17 00:00:00 2001
From: Bryce Meyer <bryce13950@gmail.com>
Date: Mon, 23 Oct 2023 17:23:58 +0200
Subject: [PATCH 09/25] updated output

---
 demos/Main_Demo.ipynb | 320 +++++++++++++++++++++++++-----------------
 1 file changed, 195 insertions(+), 125 deletions(-)

diff --git a/demos/Main_Demo.ipynb b/demos/Main_Demo.ipynb
index 22b092569..fb03ae9df 100644
--- a/demos/Main_Demo.ipynb
+++ b/demos/Main_Demo.ipynb
@@ -45,7 +45,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 121,
+            "execution_count": 137,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -78,7 +78,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 122,
+            "execution_count": 138,
             "metadata": {},
             "outputs": [
                 {
@@ -101,27 +101,27 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 123,
+            "execution_count": 139,
             "metadata": {},
             "outputs": [
                 {
                     "data": {
                         "text/html": [
-                            "<div id=\"circuits-vis-1f1775a5-9164\" style=\"margin: 15px 0;\"/>\n",
+                            "<div id=\"circuits-vis-0e7f866d-a73c\" style=\"margin: 15px 0;\"/>\n",
                             "    <script crossorigin type=\"module\">\n",
                             "    import { render, Hello } from \"https://unpkg.com/circuitsvis@1.43.0/dist/cdn/esm.js\";\n",
                             "    render(\n",
-                            "      \"circuits-vis-1f1775a5-9164\",\n",
+                            "      \"circuits-vis-0e7f866d-a73c\",\n",
                             "      Hello,\n",
                             "      {\"name\": \"Neel\"}\n",
                             "    )\n",
                             "    </script>"
                         ],
                         "text/plain": [
-                            "<circuitsvis.utils.render.RenderedHTML at 0xffff4bbf8f70>"
+                            "<circuitsvis.utils.render.RenderedHTML at 0xffff4b545e20>"
                         ]
                     },
-                    "execution_count": 123,
+                    "execution_count": 139,
                     "metadata": {
                         "text/html": {
                             "Content-Type": "text/html"
@@ -138,7 +138,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 124,
+            "execution_count": 140,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -170,7 +170,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 125,
+            "execution_count": 141,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -192,16 +192,16 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 126,
+            "execution_count": 142,
             "metadata": {},
             "outputs": [
                 {
                     "data": {
                         "text/plain": [
-                            "<torch.autograd.grad_mode.set_grad_enabled at 0xffff4a625b80>"
+                            "<torch.autograd.grad_mode.set_grad_enabled at 0xffff2c3bb520>"
                         ]
                     },
-                    "execution_count": 126,
+                    "execution_count": 142,
                     "metadata": {},
                     "output_type": "execute_result"
                 }
@@ -219,7 +219,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 127,
+            "execution_count": 143,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -267,7 +267,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 128,
+            "execution_count": 144,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -276,7 +276,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 129,
+            "execution_count": 145,
             "metadata": {},
             "outputs": [
                 {
@@ -312,7 +312,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 130,
+            "execution_count": 146,
             "metadata": {},
             "outputs": [
                 {
@@ -350,7 +350,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 131,
+            "execution_count": 147,
             "metadata": {},
             "outputs": [
                 {
@@ -384,7 +384,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 132,
+            "execution_count": 148,
             "metadata": {},
             "outputs": [
                 {
@@ -405,7 +405,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 133,
+            "execution_count": 149,
             "metadata": {},
             "outputs": [
                 {
@@ -418,21 +418,21 @@
                 {
                     "data": {
                         "text/html": [
-                            "<div id=\"circuits-vis-5be09bfe-07c2\" style=\"margin: 15px 0;\"/>\n",
+                            "<div id=\"circuits-vis-878dde93-c42c\" style=\"margin: 15px 0;\"/>\n",
                             "    <script crossorigin type=\"module\">\n",
                             "    import { render, AttentionPatterns } from \"https://unpkg.com/circuitsvis@1.43.0/dist/cdn/esm.js\";\n",
                             "    render(\n",
-                            "      \"circuits-vis-5be09bfe-07c2\",\n",
+                            "      \"circuits-vis-878dde93-c42c\",\n",
                             "      AttentionPatterns,\n",
                             "      {\"tokens\": [\"<|endoftext|>\", \"Natural\", \" language\", \" processing\", \" tasks\", \",\", \" such\", \" as\", \" question\", \" answering\", \",\", \" machine\", \" translation\", \",\", \" reading\", \" comprehension\", \",\", \" and\", \" summar\", \"ization\", \",\", \" are\", \" typically\", \" approached\", \" with\", \" supervised\", \" learning\", \" on\", \" tasks\", \"pe\", \"cific\", \" datasets\", \".\"], \"attention\": [[[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.9639418125152588, 0.03605816140770912, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.8389372825622559, 0.11828784644603729, 0.042774833738803864, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 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                             "                            \n",
-                            "var gd = document.getElementById('2a1b6d01-c746-47ef-aedf-b3845cebfac9');\n",
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@@ -917,24 +917,24 @@
         },
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             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 156,
             "metadata": {},
             "outputs": [
                 {
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                         "text/plain": [
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@@ -998,7 +998,7 @@
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+            "execution_count": 157,
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@@ -1203,7 +1203,7 @@
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@@ -1240,7 +1240,7 @@
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@@ -1378,7 +1378,7 @@
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@@ -1730,7 +1730,7 @@
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@@ -1902,8 +1902,8 @@
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                     "text": [
                         "Norms:\n",
-                        "tensor(9.2448)\n",
-                        "tensor(9.2448)\n",
+                        "tensor(7.9287)\n",
+                        "tensor(7.9287)\n",
                         "Right dimension: 5, Left dimension: 5, Hidden dimension: 2\n"
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@@ -1929,7 +1929,7 @@
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@@ -1937,13 +1937,13 @@
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                         "Eigenvalues:\n",
-                        "tensor([-2.9891e+00+0.j,  9.8913e-09+0.j, -4.8101e-01+0.j,  1.7593e-07+0.j,\n",
-                        "        -4.8844e-08+0.j])\n",
-                        "tensor([-2.9891+0.j, -0.4810+0.j])\n",
+                        "tensor([-1.1366e+00+3.9174j, -1.1366e+00-3.9174j, -7.2454e-08+0.0000j,\n",
+                        "        -1.9419e-07+0.0000j, -2.4057e-08+0.0000j])\n",
+                        "tensor([-1.1366+3.9174j, -1.1366-3.9174j])\n",
                         "\n",
                         "Singular Values:\n",
-                        "tensor([8.9167e+00, 2.4411e+00, 4.6067e-07, 6.3512e-08, 3.8117e-09])\n",
-                        "tensor([8.9167, 2.4411])\n"
+                        "tensor([7.3159e+00, 3.0565e+00, 1.1441e-06, 1.3619e-07, 8.8477e-09])\n",
+                        "tensor([7.3159, 3.0565])\n"
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@@ -1966,15 +1966,15 @@
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-            "execution_count": null,
+            "execution_count": 177,
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                     "name": "stdout",
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                     "text": [
-                        "Unfactored: torch.Size([5, 300]) tensor(153.5882)\n",
-                        "Factored: torch.Size([5, 300]) tensor(153.5882)\n",
+                        "Unfactored: torch.Size([5, 300]) tensor(139.1795)\n",
+                        "Factored: torch.Size([5, 300]) tensor(139.1796)\n",
                         "Right dimension: 300, Left dimension: 5, Hidden dimension: 2\n"
                     ]
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@@ -1997,7 +1997,7 @@
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@@ -2040,7 +2040,7 @@
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@@ -2058,7 +2058,7 @@
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@@ -2078,7 +2078,7 @@
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                             "                            \n",
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@@ -2335,7 +2335,7 @@
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             "cell_type": "code",
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+            "execution_count": 187,
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                 {
@@ -2372,13 +2372,13 @@
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+            "execution_count": 188,
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                 {
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                         "application/vnd.jupyter.widget-view+json": {
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@@ -2392,10 +2392,10 @@
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-                            "'(CNN) President Barack Obama caught in embarrassing new scandal\\n\\nThe president had an affair with a young woman\\n\\nThe White House press secretary said her comments were \"disgusting\" and that they were \"disgusting\" because she denied the allegations\\n\\n\"I have to say, I think'"
+                            "\"(CNN) President Barack Obama caught in embarrassing new scandal\\n\\nIt's not the first time Obama has been caught in a scandal, but it doesn't seem like it's the last.\\n\\nIn an interview with the Huffington Post, Obama said his late wife, Michelle, had been caught buying alcohol and\""
                         ]
                     },
-                    "execution_count": 113,
+                    "execution_count": 188,
                     "metadata": {},
                     "output_type": "execute_result"
                 }
@@ -2450,7 +2450,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 189,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -2508,7 +2508,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 190,
             "metadata": {},
             "outputs": [
                 {
@@ -2543,7 +2543,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 191,
             "metadata": {},
             "outputs": [
                 {
@@ -2610,7 +2610,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 192,
             "metadata": {},
             "outputs": [
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@@ -2620,9 +2620,9 @@
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Values for solu-6l-pile (Linear scale)\"}},                        {\"responsive\": true}                    ).then(function(){\n",
                             "                            \n",
-                            "var gd = document.getElementById('a9d47cb2-6d92-4cec-a2a6-066f57a4e496');\n",
+                            "var gd = document.getElementById('0141b548-4a98-441b-9c46-d391d2b7e71a');\n",
                             "var x = new MutationObserver(function (mutations, observer) {{\n",
                             "        var display = window.getComputedStyle(gd).display;\n",
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@@ -2850,7 +2850,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 193,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -2873,7 +2873,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 194,
             "metadata": {},
             "outputs": [
                 {
@@ -2884,17 +2884,87 @@
                     ]
                 },
                 {
-                    "ename": "AssertionError",
-                    "evalue": "Torch not compiled with CUDA enabled",
-                    "output_type": "error",
-                    "traceback": [
-                        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-                        "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
-                        "\u001b[1;32m/workspaces/TransformerLens/demos/Main_Demo.ipynb Cell 142\u001b[0m line \u001b[0;36m9\n\u001b[1;32m      <a href='vscode-notebook-cell://dev-container%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_Demo.ipynb#Y261sdnNjb2RlLXJlbW90ZQ%3D%3D?line=5'>6</a>\u001b[0m tokens_seen_for_this_checkpoint \u001b[39m=\u001b[39m model_for_this_checkpoint\u001b[39m.\u001b[39mcfg\u001b[39m.\u001b[39mcheckpoint_value\n\u001b[1;32m      <a href='vscode-notebook-cell://dev-container%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_Demo.ipynb#Y261sdnNjb2RlLXJlbW90ZQ%3D%3D?line=6'>7</a>\u001b[0m tokens_trained_on\u001b[39m.\u001b[39mappend(tokens_seen_for_this_checkpoint)\n\u001b[0;32m----> <a href='vscode-notebook-cell://dev-container%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_Demo.ipynb#Y261sdnNjb2RlLXJlbW90ZQ%3D%3D?line=8'>9</a>\u001b[0m induction_loss_for_this_checkpoint \u001b[39m=\u001b[39m evals\u001b[39m.\u001b[39;49minduction_loss(model_for_this_checkpoint)\u001b[39m.\u001b[39mitem()\n\u001b[1;32m     <a href='vscode-notebook-cell://dev-container%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_Demo.ipynb#Y261sdnNjb2RlLXJlbW90ZQ%3D%3D?line=9'>10</a>\u001b[0m induction_losses\u001b[39m.\u001b[39mappend(induction_loss_for_this_checkpoint)\n",
-                        "File \u001b[0;32m/workspaces/TransformerLens/.venv/lib/python3.8/site-packages/torch/utils/_contextlib.py:115\u001b[0m, in \u001b[0;36mcontext_decorator.<locals>.decorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    112\u001b[0m \u001b[39m@functools\u001b[39m\u001b[39m.\u001b[39mwraps(func)\n\u001b[1;32m    113\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mdecorate_context\u001b[39m(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m    114\u001b[0m     \u001b[39mwith\u001b[39;00m ctx_factory():\n\u001b[0;32m--> 115\u001b[0m         \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
-                        "File \u001b[0;32m/workspaces/TransformerLens/transformer_lens/evals.py:133\u001b[0m, in \u001b[0;36minduction_loss\u001b[0;34m(model, tokenizer, batch_size, subseq_len, prepend_bos, device)\u001b[0m\n\u001b[1;32m    126\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    127\u001b[0m \u001b[39mGenerates a batch of random sequences repeated twice, and measures model performance on the second half. Tests whether a model has induction heads.\u001b[39;00m\n\u001b[1;32m    128\u001b[0m \n\u001b[1;32m    129\u001b[0m \u001b[39mBy default, prepends a beginning of string token (when prepend_bos flag defaults to None, model.cfg.default_prepend_bos is used\u001b[39;00m\n\u001b[1;32m    130\u001b[0m \u001b[39mwhose default is True unless specified otherwise), which is useful to give models a resting position, and sometimes models were trained with this.\u001b[39;00m\n\u001b[1;32m    131\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    132\u001b[0m \u001b[39m# Make the repeated sequence\u001b[39;00m\n\u001b[0;32m--> 133\u001b[0m first_half_tokens \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39;49mrandint(\u001b[39m100\u001b[39;49m, \u001b[39m20000\u001b[39;49m, (batch_size, subseq_len))\u001b[39m.\u001b[39;49mto(device)\n\u001b[1;32m    134\u001b[0m repeated_tokens \u001b[39m=\u001b[39m einops\u001b[39m.\u001b[39mrepeat(first_half_tokens, \u001b[39m\"\u001b[39m\u001b[39mb p -> b (2 p)\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m    136\u001b[0m \u001b[39m# Use the provided prepend_bos as an override if it's not None;\u001b[39;00m\n\u001b[1;32m    137\u001b[0m \u001b[39m# otherwise use model.cfg.default_prepend_bos (defaults to True)\u001b[39;00m\n",
-                        "File \u001b[0;32m/workspaces/TransformerLens/.venv/lib/python3.8/site-packages/torch/cuda/__init__.py:289\u001b[0m, in \u001b[0;36m_lazy_init\u001b[0;34m()\u001b[0m\n\u001b[1;32m    284\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mRuntimeError\u001b[39;00m(\n\u001b[1;32m    285\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mCannot re-initialize CUDA in forked subprocess. To use CUDA with \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    286\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mmultiprocessing, you must use the \u001b[39m\u001b[39m'\u001b[39m\u001b[39mspawn\u001b[39m\u001b[39m'\u001b[39m\u001b[39m start method\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    287\u001b[0m     )\n\u001b[1;32m    288\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mhasattr\u001b[39m(torch\u001b[39m.\u001b[39m_C, \u001b[39m\"\u001b[39m\u001b[39m_cuda_getDeviceCount\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[0;32m--> 289\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mAssertionError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mTorch not compiled with CUDA enabled\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m    290\u001b[0m \u001b[39mif\u001b[39;00m _cudart \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    291\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mAssertionError\u001b[39;00m(\n\u001b[1;32m    292\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mlibcudart functions unavailable. It looks like you have a broken build?\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    293\u001b[0m     )\n",
-                        "\u001b[0;31mAssertionError\u001b[0m: Torch not compiled with CUDA enabled"
+                    "data": {
+                        "application/vnd.jupyter.widget-view+json": {
+                            "model_id": "e0cefe71824247e2a276d27c01f91c18",
+                            "version_major": 2,
+                            "version_minor": 0
+                        },
+                        "text/plain": [
+                            "tokens_000187432960.pth:   0%|          | 0.00/227M [00:00<?, ?B/s]"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Loaded pretrained model solu-2l into HookedTransformer\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "application/vnd.jupyter.widget-view+json": {
+                            "model_id": "a228317dd48f480480a8d5b6f814f488",
+                            "version_major": 2,
+                            "version_minor": 0
+                        },
+                        "text/plain": [
+                            "tokens_000528482304.pth:   0%|          | 0.00/227M [00:00<?, ?B/s]"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Loaded pretrained model solu-2l into HookedTransformer\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "application/vnd.jupyter.widget-view+json": {
+                            "model_id": "a43460e2256e40359e512a57a9557efc",
+                            "version_major": 2,
+                            "version_minor": 0
+                        },
+                        "text/plain": [
+                            "tokens_001628438528.pth:   0%|          | 0.00/227M [00:00<?, ?B/s]"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Loaded pretrained model solu-2l into HookedTransformer\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "application/vnd.jupyter.widget-view+json": {
+                            "model_id": "94e234fd3b224f09b1d74697574ec6a8",
+                            "version_major": 2,
+                            "version_minor": 0
+                        },
+                        "text/plain": [
+                            "tokens_021780496384.pth:   0%|          | 0.00/227M [00:00<?, ?B/s]"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Loaded pretrained model solu-2l into HookedTransformer\n"
                     ]
                 }
             ],
@@ -2922,7 +2992,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 195,
             "metadata": {},
             "outputs": [
                 {
@@ -2932,9 +3002,9 @@
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Trained On\"},\"type\":\"log\"},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"index\"}},\"legend\":{\"tracegroupgap\":0},\"title\":{\"text\":\"Induction Loss over training: solu-2l\"}},                        {\"responsive\": true}                    ).then(function(){\n",
                             "                            \n",
-                            "var gd = document.getElementById('6e70a192-5d22-459b-9b0e-f6d00be0bd1e');\n",
+                            "var gd = document.getElementById('8892dfa3-6f16-4b39-baef-ba9fdc262982');\n",
                             "var x = new MutationObserver(function (mutations, observer) {{\n",
                             "        var display = window.getComputedStyle(gd).display;\n",
                             "        if (!display || display === 'none') {{\n",

From 13fa523c60481dc2218b679847d04a0447fdbafe Mon Sep 17 00:00:00 2001
From: Bryce Meyer <bryce13950@gmail.com>
Date: Mon, 23 Oct 2023 18:13:48 +0200
Subject: [PATCH 10/25] set default device

---
 demos/Main_Demo.ipynb | 16 +++++-----------
 1 file changed, 5 insertions(+), 11 deletions(-)

diff --git a/demos/Main_Demo.ipynb b/demos/Main_Demo.ipynb
index fb03ae9df..d361071e6 100644
--- a/demos/Main_Demo.ipynb
+++ b/demos/Main_Demo.ipynb
@@ -267,16 +267,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 144,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "device = \"cuda\" if torch.cuda.is_available() else \"cpu\""
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 145,
+            "execution_count": 196,
             "metadata": {},
             "outputs": [
                 {
@@ -295,7 +286,10 @@
                 }
             ],
             "source": [
-                "model = HookedTransformer.from_pretrained(\"gpt2-small\", device=device)"
+                "model = HookedTransformer.from_pretrained(\"gpt2-small\", default_prepend_bos=False)\n",
+                "\n",
+                "# Get the default device used\n",
+                "device: torch.device = utils.get_device()"
             ]
         },
         {

From 5867833f1707bad303ac48cb4c4631a211a9c502 Mon Sep 17 00:00:00 2001
From: Bryce Meyer <bryce13950@gmail.com>
Date: Mon, 23 Oct 2023 18:21:05 +0200
Subject: [PATCH 11/25] removed pulling device from utils

---
 demos/Main_Demo.ipynb | 17 +++++++++++------
 1 file changed, 11 insertions(+), 6 deletions(-)

diff --git a/demos/Main_Demo.ipynb b/demos/Main_Demo.ipynb
index d361071e6..62feb294e 100644
--- a/demos/Main_Demo.ipynb
+++ b/demos/Main_Demo.ipynb
@@ -49,7 +49,6 @@
             "metadata": {},
             "outputs": [],
             "source": [
-                "import os\n",
                 "DEVELOPMENT_MODE = False\n",
                 "# Detect if we're running in Google Colab\n",
                 "try:\n",
@@ -267,7 +266,16 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 196,
+            "execution_count": 144,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "device = \"cuda\" if torch.cuda.is_available() else \"cpu\""
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 198,
             "metadata": {},
             "outputs": [
                 {
@@ -286,10 +294,7 @@
                 }
             ],
             "source": [
-                "model = HookedTransformer.from_pretrained(\"gpt2-small\", default_prepend_bos=False)\n",
-                "\n",
-                "# Get the default device used\n",
-                "device: torch.device = utils.get_device()"
+                "model = HookedTransformer.from_pretrained(\"gpt2-small\", device=device)"
             ]
         },
         {

From 4ce46fa2f1093a1bef0b40ce3738ec0a7a4ffd28 Mon Sep 17 00:00:00 2001
From: Bryce Meyer <bryce13950@gmail.com>
Date: Mon, 23 Oct 2023 18:21:58 +0200
Subject: [PATCH 12/25] ignored loading output

---
 demos/Main_Demo.ipynb | 1 +
 1 file changed, 1 insertion(+)

diff --git a/demos/Main_Demo.ipynb b/demos/Main_Demo.ipynb
index 62feb294e..7a39b8586 100644
--- a/demos/Main_Demo.ipynb
+++ b/demos/Main_Demo.ipynb
@@ -294,6 +294,7 @@
                 }
             ],
             "source": [
+                "# NBVAL_IGNORE_OUTPUT\n",
                 "model = HookedTransformer.from_pretrained(\"gpt2-small\", device=device)"
             ]
         },

From ba01a0a91b1ad0118864c7d306ae9e8c35068ceb Mon Sep 17 00:00:00 2001
From: Bryce Meyer <bryce13950@gmail.com>
Date: Mon, 23 Oct 2023 19:01:54 +0200
Subject: [PATCH 13/25] changed input tensor to be the same in github instead
 of random

---
 demos/Main_Demo.ipynb | 18 ++++++++++++++----
 1 file changed, 14 insertions(+), 4 deletions(-)

diff --git a/demos/Main_Demo.ipynb b/demos/Main_Demo.ipynb
index 7a39b8586..bc05f69c2 100644
--- a/demos/Main_Demo.ipynb
+++ b/demos/Main_Demo.ipynb
@@ -49,6 +49,7 @@
             "metadata": {},
             "outputs": [],
             "source": [
+                "import os\n",
                 "DEVELOPMENT_MODE = False\n",
                 "# Detect if we're running in Google Colab\n",
                 "try:\n",
@@ -72,6 +73,8 @@
                 "    if not ip.extension_manager.loaded:\n",
                 "        ip.extension_manager.load('autoreload')\n",
                 "        %autoreload 2\n",
+                "        \n",
+                "IN_GITHUB = os.getenv(\"GITHUB_ACTIONS\") == \"true\"\n",
                 "    "
             ]
         },
@@ -761,7 +764,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 154,
+            "execution_count": 205,
             "metadata": {},
             "outputs": [
                 {
@@ -771,9 +774,9 @@
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                             "<body>\n",
                             "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
-                            "        <script charset=\"utf-8\" src=\"https://cdn.plot.ly/plotly-2.26.0.min.js\"></script>                <div id=\"4277f280-d718-49f3-aaa2-f35dda7dd3c1\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"4277f280-d718-49f3-aaa2-f35dda7dd3c1\")) {                    Plotly.newPlot(                        \"4277f280-d718-49f3-aaa2-f35dda7dd3c1\",                        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by position on random repeated tokens\"}},                        {\"responsive\": true}                    ).then(function(){\n",
                             "                            \n",
-                            "var gd = document.getElementById('4277f280-d718-49f3-aaa2-f35dda7dd3c1');\n",
+                            "var gd = document.getElementById('0f830d06-d43b-4b9e-87b4-d5f978e44cbf');\n",
                             "var x = new MutationObserver(function (mutations, observer) {{\n",
                             "        var display = window.getComputedStyle(gd).display;\n",
                             "        if (!display || display === 'none') {{\n",
@@ -807,7 +810,14 @@
             "source": [
                 "batch_size = 10\n",
                 "seq_len = 50\n",
-                "random_tokens = torch.randint(1000, 10000, (batch_size, seq_len)).to(model.cfg.device)\n",
+                "size = (batch_size, seq_len)\n",
+                "if not IN_GITHUB:\n",
+                "    input_tensor = torch.randint(1000, 10000, size)\n",
+                "else: \n",
+                "    numbers = torch.arange(1, 501, 1)\n",
+                "    input_tensor = numbers.reshape(size)\n",
+                "\n",
+                "random_tokens = input_tensor.to(model.cfg.device)\n",
                 "repeated_tokens = einops.repeat(random_tokens, \"batch seq_len -> batch (2 seq_len)\")\n",
                 "repeated_logits = model(repeated_tokens)\n",
                 "correct_log_probs = model.loss_fn(repeated_logits, repeated_tokens, per_token=True)\n",

From 3c53ce2dabb26b022ed12bc321a34f88cec367aa Mon Sep 17 00:00:00 2001
From: Bryce Meyer <bryce13950@gmail.com>
Date: Mon, 23 Oct 2023 19:13:48 +0200
Subject: [PATCH 14/25] regenerated output

---
 demos/Main_Demo.ipynb | 331 +++++++++++++++++-------------------------
 1 file changed, 131 insertions(+), 200 deletions(-)

diff --git a/demos/Main_Demo.ipynb b/demos/Main_Demo.ipynb
index bc05f69c2..4ef3801e8 100644
--- a/demos/Main_Demo.ipynb
+++ b/demos/Main_Demo.ipynb
@@ -45,7 +45,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 137,
+            "execution_count": 206,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -80,7 +80,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 138,
+            "execution_count": 207,
             "metadata": {},
             "outputs": [
                 {
@@ -103,27 +103,27 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 139,
+            "execution_count": 208,
             "metadata": {},
             "outputs": [
                 {
                     "data": {
                         "text/html": [
-                            "<div id=\"circuits-vis-0e7f866d-a73c\" style=\"margin: 15px 0;\"/>\n",
+                            "<div id=\"circuits-vis-07f7a552-4036\" style=\"margin: 15px 0;\"/>\n",
                             "    <script crossorigin type=\"module\">\n",
                             "    import { render, Hello } from \"https://unpkg.com/circuitsvis@1.43.0/dist/cdn/esm.js\";\n",
                             "    render(\n",
-                            "      \"circuits-vis-0e7f866d-a73c\",\n",
+                            "      \"circuits-vis-07f7a552-4036\",\n",
                             "      Hello,\n",
                             "      {\"name\": \"Neel\"}\n",
                             "    )\n",
                             "    </script>"
                         ],
                         "text/plain": [
-                            "<circuitsvis.utils.render.RenderedHTML at 0xffff4b545e20>"
+                            "<circuitsvis.utils.render.RenderedHTML at 0xffff14280a60>"
                         ]
                     },
-                    "execution_count": 139,
+                    "execution_count": 208,
                     "metadata": {
                         "text/html": {
                             "Content-Type": "text/html"
@@ -140,7 +140,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 140,
+            "execution_count": 209,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -172,7 +172,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 141,
+            "execution_count": 210,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -194,16 +194,16 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 142,
+            "execution_count": 211,
             "metadata": {},
             "outputs": [
                 {
                     "data": {
                         "text/plain": [
-                            "<torch.autograd.grad_mode.set_grad_enabled at 0xffff2c3bb520>"
+                            "<torch.autograd.grad_mode.set_grad_enabled at 0xffff0e7c3b50>"
                         ]
                     },
-                    "execution_count": 142,
+                    "execution_count": 211,
                     "metadata": {},
                     "output_type": "execute_result"
                 }
@@ -221,7 +221,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 143,
+            "execution_count": 212,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -269,7 +269,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 144,
+            "execution_count": 213,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -278,7 +278,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 198,
+            "execution_count": 214,
             "metadata": {},
             "outputs": [
                 {
@@ -315,7 +315,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 146,
+            "execution_count": 215,
             "metadata": {},
             "outputs": [
                 {
@@ -353,7 +353,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 147,
+            "execution_count": 216,
             "metadata": {},
             "outputs": [
                 {
@@ -387,7 +387,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 148,
+            "execution_count": 217,
             "metadata": {},
             "outputs": [
                 {
@@ -408,7 +408,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 149,
+            "execution_count": 218,
             "metadata": {},
             "outputs": [
                 {
@@ -421,21 +421,21 @@
                 {
                     "data": {
                         "text/html": [
-                            "<div id=\"circuits-vis-878dde93-c42c\" style=\"margin: 15px 0;\"/>\n",
+                            "<div id=\"circuits-vis-665bd171-128d\" style=\"margin: 15px 0;\"/>\n",
                             "    <script crossorigin type=\"module\">\n",
                             "    import { render, AttentionPatterns } from \"https://unpkg.com/circuitsvis@1.43.0/dist/cdn/esm.js\";\n",
                             "    render(\n",
-                            "      \"circuits-vis-878dde93-c42c\",\n",
+                            "      \"circuits-vis-665bd171-128d\",\n",
                             "      AttentionPatterns,\n",
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@@ -489,7 +489,7 @@
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@@ -617,13 +617,13 @@
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@@ -677,7 +677,7 @@
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                             "                            \n",
-                            "var gd = document.getElementById('8390b047-133f-45f8-89e9-ba6ef03f0939');\n",
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@@ -927,24 +927,24 @@
         },
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             "cell_type": "code",
-            "execution_count": 156,
+            "execution_count": 225,
             "metadata": {},
             "outputs": [
                 {
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                             "    )\n",
                             "    </script>"
                         ],
                         "text/plain": [
-                            "<circuitsvis.utils.render.RenderedHTML at 0xffff144d8fa0>"
+                            "<circuitsvis.utils.render.RenderedHTML at 0xffff14610be0>"
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                     },
                     "metadata": {
@@ -1008,7 +1008,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 157,
+            "execution_count": 226,
             "metadata": {},
             "outputs": [
                 {
@@ -1024,7 +1024,18 @@
                     "text": [
                         "Loaded pretrained model distilgpt2 into HookedTransformer\n"
                     ]
-                },
+                }
+            ],
+            "source": [
+                "# NBVAL_IGNORE_OUTPUT\n",
+                "distilgpt2 = HookedTransformer.from_pretrained(\"distilgpt2\", device=device)"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 227,
+            "metadata": {},
+            "outputs": [
                 {
                     "data": {
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                             "                            \n",
-                            "var gd = document.getElementById('fc07613b-eaf0-40e7-987e-56ac9b21c42a');\n",
+                            "var gd = document.getElementById('0007eeb2-46f5-48b7-b7bc-e13400c54a25');\n",
                             "var x = new MutationObserver(function (mutations, observer) {{\n",
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@@ -1066,7 +1077,7 @@
                 }
             ],
             "source": [
-                "distilgpt2 = HookedTransformer.from_pretrained(\"distilgpt2\")\n",
+                "\n",
                 "# We make a tensor to store the induction score for each head. We put it on the model's device to avoid needing to move things between the GPU and CPU, which can be slow.\n",
                 "distilgpt2_induction_score_store = torch.zeros((distilgpt2.cfg.n_layers, distilgpt2.cfg.n_heads), device=distilgpt2.cfg.device)\n",
                 "def induction_score_hook(\n",
@@ -1213,7 +1224,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 158,
+            "execution_count": 228,
             "metadata": {},
             "outputs": [
                 {
@@ -1250,7 +1261,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 159,
+            "execution_count": 229,
             "metadata": {},
             "outputs": [
                 {
@@ -1288,7 +1299,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 160,
+            "execution_count": 230,
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             "outputs": [
                 {
@@ -1378,7 +1389,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 161,
+            "execution_count": 231,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -1388,7 +1399,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 162,
+            "execution_count": 232,
             "metadata": {},
             "outputs": [
                 {
@@ -1462,7 +1473,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 163,
+            "execution_count": 233,
             "metadata": {},
             "outputs": [
                 {
@@ -1523,7 +1534,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 164,
+            "execution_count": 234,
             "metadata": {},
             "outputs": [
                 {
@@ -1549,7 +1560,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 165,
+            "execution_count": 235,
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                 {
@@ -1582,7 +1593,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 166,
+            "execution_count": 236,
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             "outputs": [
                 {
@@ -1618,7 +1629,7 @@
         },
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             "cell_type": "code",
-            "execution_count": 167,
+            "execution_count": 237,
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             "outputs": [
                 {
@@ -1651,7 +1662,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 168,
+            "execution_count": 238,
             "metadata": {},
             "outputs": [
                 {
@@ -1682,7 +1693,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 169,
+            "execution_count": 239,
             "metadata": {},
             "outputs": [
                 {
@@ -1708,7 +1719,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 170,
+            "execution_count": 240,
             "metadata": {},
             "outputs": [
                 {
@@ -1740,7 +1751,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 171,
+            "execution_count": 241,
             "metadata": {},
             "outputs": [
                 {
@@ -1784,7 +1795,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 172,
+            "execution_count": 242,
             "metadata": {},
             "outputs": [
                 {
@@ -1818,7 +1829,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 173,
+            "execution_count": 243,
             "metadata": {},
             "outputs": [
                 {
@@ -1851,7 +1862,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 174,
+            "execution_count": 244,
             "metadata": {},
             "outputs": [
                 {
@@ -1904,7 +1915,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 175,
+            "execution_count": 245,
             "metadata": {},
             "outputs": [
                 {
@@ -1912,8 +1923,8 @@
                     "output_type": "stream",
                     "text": [
                         "Norms:\n",
-                        "tensor(7.9287)\n",
-                        "tensor(7.9287)\n",
+                        "tensor(6.6564)\n",
+                        "tensor(6.6564)\n",
                         "Right dimension: 5, Left dimension: 5, Hidden dimension: 2\n"
                     ]
                 }
@@ -1939,7 +1950,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 176,
+            "execution_count": 246,
             "metadata": {},
             "outputs": [
                 {
@@ -1947,13 +1958,13 @@
                     "output_type": "stream",
                     "text": [
                         "Eigenvalues:\n",
-                        "tensor([-1.1366e+00+3.9174j, -1.1366e+00-3.9174j, -7.2454e-08+0.0000j,\n",
-                        "        -1.9419e-07+0.0000j, -2.4057e-08+0.0000j])\n",
-                        "tensor([-1.1366+3.9174j, -1.1366-3.9174j])\n",
+                        "tensor([ 1.0657e-07+0.0000j,  1.8773e+00+1.4089j,  1.8773e+00-1.4089j,\n",
+                        "        -2.1421e-08+0.0000j,  9.2447e-08+0.0000j])\n",
+                        "tensor([1.8773+1.4089j, 1.8773-1.4089j])\n",
                         "\n",
                         "Singular Values:\n",
-                        "tensor([7.3159e+00, 3.0565e+00, 1.1441e-06, 1.3619e-07, 8.8477e-09])\n",
-                        "tensor([7.3159, 3.0565])\n"
+                        "tensor([6.1567e+00, 2.5303e+00, 2.5031e-07, 3.7791e-08, 4.8972e-09])\n",
+                        "tensor([6.1567, 2.5303])\n"
                     ]
                 }
             ],
@@ -1976,15 +1987,15 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 177,
+            "execution_count": 247,
             "metadata": {},
             "outputs": [
                 {
                     "name": "stdout",
                     "output_type": "stream",
                     "text": [
-                        "Unfactored: torch.Size([5, 300]) tensor(139.1795)\n",
-                        "Factored: torch.Size([5, 300]) tensor(139.1796)\n",
+                        "Unfactored: torch.Size([5, 300]) tensor(118.8196)\n",
+                        "Factored: torch.Size([5, 300]) tensor(118.8196)\n",
                         "Right dimension: 300, Left dimension: 5, Hidden dimension: 2\n"
                     ]
                 }
@@ -2007,7 +2018,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 178,
+            "execution_count": 248,
             "metadata": {},
             "outputs": [
                 {
@@ -2050,7 +2061,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 179,
+            "execution_count": 249,
             "metadata": {},
             "outputs": [
                 {
@@ -2068,7 +2079,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 180,
+            "execution_count": 250,
             "metadata": {},
             "outputs": [
                 {
@@ -2088,7 +2099,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 181,
+            "execution_count": 251,
             "metadata": {},
             "outputs": [
                 {
@@ -2098,9 +2109,9 @@
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+            "execution_count": 252,
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                             "                            \n",
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@@ -2345,7 +2356,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 187,
+            "execution_count": 257,
             "metadata": {},
             "outputs": [
                 {
@@ -2382,13 +2393,13 @@
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         {
             "cell_type": "code",
-            "execution_count": 188,
+            "execution_count": 258,
             "metadata": {},
             "outputs": [
                 {
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                         "application/vnd.jupyter.widget-view+json": {
-                            "model_id": "6a6dd86e29e3490591ab18725a541359",
+                            "model_id": "9bb06a67aba24149b5d2161d3acfde6c",
                             "version_major": 2,
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@@ -2402,10 +2413,10 @@
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                     "data": {
                         "text/plain": [
-                            "\"(CNN) President Barack Obama caught in embarrassing new scandal\\n\\nIt's not the first time Obama has been caught in a scandal, but it doesn't seem like it's the last.\\n\\nIn an interview with the Huffington Post, Obama said his late wife, Michelle, had been caught buying alcohol and\""
+                            "'(CNN) President Barack Obama caught in embarrassing new scandal\\n\\nThe president\\'s infamous \"political adviser\" is now suing the Republican National Committee for the alleged negligence of his wife, accusing the group of \"trying to make her look bad.\"\\n\\nThe lawsuit, filed on Thursday in federal court in Washington'"
                         ]
                     },
-                    "execution_count": 188,
+                    "execution_count": 258,
                     "metadata": {},
                     "output_type": "execute_result"
                 }
@@ -2460,7 +2471,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 189,
+            "execution_count": 259,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -2518,7 +2529,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 190,
+            "execution_count": 260,
             "metadata": {},
             "outputs": [
                 {
@@ -2553,7 +2564,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 191,
+            "execution_count": 261,
             "metadata": {},
             "outputs": [
                 {
@@ -2620,7 +2631,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 192,
+            "execution_count": 262,
             "metadata": {},
             "outputs": [
                 {
@@ -2630,9 +2641,9 @@
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+                        "Loaded pretrained model solu-2l into HookedTransformer\n",
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                             "                            \n",
-                            "var gd = document.getElementById('8892dfa3-6f16-4b39-baef-ba9fdc262982');\n",
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From b8fa8b8c051719c13ee4278699dd2cffa017d272 Mon Sep 17 00:00:00 2001
From: Bryce Meyer <bryce13950@gmail.com>
Date: Mon, 23 Oct 2023 19:38:16 +0200
Subject: [PATCH 15/25] updated random tests to be static

---
 demos/Main_Demo.ipynb | 258 +++++++++++++++++++++++-------------------
 1 file changed, 139 insertions(+), 119 deletions(-)

diff --git a/demos/Main_Demo.ipynb b/demos/Main_Demo.ipynb
index 4ef3801e8..f24e2dc50 100644
--- a/demos/Main_Demo.ipynb
+++ b/demos/Main_Demo.ipynb
@@ -45,7 +45,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 206,
+            "execution_count": 272,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -75,12 +75,13 @@
                 "        %autoreload 2\n",
                 "        \n",
                 "IN_GITHUB = os.getenv(\"GITHUB_ACTIONS\") == \"true\"\n",
+                "IN_GITHUB = True\n",
                 "    "
             ]
         },
         {
             "cell_type": "code",
-            "execution_count": 207,
+            "execution_count": 273,
             "metadata": {},
             "outputs": [
                 {
@@ -103,27 +104,27 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 208,
+            "execution_count": 274,
             "metadata": {},
             "outputs": [
                 {
                     "data": {
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                             "                            \n",
-                            "var gd = document.getElementById('185a4f97-26fc-4c8d-9984-5c0a9d08a333');\n",
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@@ -927,24 +928,24 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 225,
+            "execution_count": 300,
             "metadata": {},
             "outputs": [
                 {
                     "data": {
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                             "    )\n",
                             "    </script>"
                         ],
                         "text/plain": [
-                            "<circuitsvis.utils.render.RenderedHTML at 0xffff14610be0>"
+                            "<circuitsvis.utils.render.RenderedHTML at 0xffff2cf54850>"
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                     },
                     "metadata": {
@@ -958,7 +959,14 @@
             "source": [
                 "induction_head_layer = 5\n",
                 "induction_head_index = 5\n",
-                "single_random_sequence = torch.randint(1000, 10000, (1, 20)).to(model.cfg.device)\n",
+                "size = (1, 20)\n",
+                "if not IN_GITHUB:\n",
+                "    input_tensor = torch.randint(1000, 10000, size)\n",
+                "else: \n",
+                "    numbers = torch.arange(1, 501, 500/20, dtype=torch.int)\n",
+                "    input_tensor = numbers.reshape(size)\n",
+                "\n",
+                "single_random_sequence = input_tensor.to(model.cfg.device)\n",
                 "repeated_random_sequence = einops.repeat(single_random_sequence, \"batch seq_len -> batch (2 seq_len)\")\n",
                 "def visualize_pattern_hook(\n",
                 "    pattern: Float[torch.Tensor, \"batch head_index dest_pos source_pos\"],\n",
@@ -1008,7 +1016,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 226,
+            "execution_count": 301,
             "metadata": {},
             "outputs": [
                 {
@@ -1033,7 +1041,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 227,
+            "execution_count": 302,
             "metadata": {},
             "outputs": [
                 {
@@ -1043,9 +1051,9 @@
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mbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"scaleanchor\":\"y\",\"constrain\":\"domain\",\"title\":{\"text\":\"Head\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"autorange\":\"reversed\",\"constrain\":\"domain\",\"title\":{\"text\":\"Layer\"}},\"coloraxis\":{\"colorscale\":[[0.0,\"rgb(103,0,31)\"],[0.1,\"rgb(178,24,43)\"],[0.2,\"rgb(214,96,77)\"],[0.3,\"rgb(244,165,130)\"],[0.4,\"rgb(253,219,199)\"],[0.5,\"rgb(247,247,247)\"],[0.6,\"rgb(209,229,240)\"],[0.7,\"rgb(146,197,222)\"],[0.8,\"rgb(67,147,195)\"],[0.9,\"rgb(33,102,172)\"],[1.0,\"rgb(5,48,97)\"]],\"cmid\":0.0},\"title\":{\"text\":\"Induction Score by Head in Distil GPT-2\"}},                        {\"responsive\": true}                    ).then(function(){\n",
                             "                            \n",
-                            "var gd = document.getElementById('0007eeb2-46f5-48b7-b7bc-e13400c54a25');\n",
+                            "var gd = document.getElementById('f4333c94-b236-46ab-949c-e61a3b8b4640');\n",
                             "var x = new MutationObserver(function (mutations, observer) {{\n",
                             "        var display = window.getComputedStyle(gd).display;\n",
                             "        if (!display || display === 'none') {{\n",
@@ -1224,7 +1232,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 228,
+            "execution_count": 303,
             "metadata": {},
             "outputs": [
                 {
@@ -1261,7 +1269,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 229,
+            "execution_count": 304,
             "metadata": {},
             "outputs": [
                 {
@@ -1299,7 +1307,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 230,
+            "execution_count": 305,
             "metadata": {},
             "outputs": [
                 {
@@ -1389,7 +1397,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 231,
+            "execution_count": 306,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -1399,7 +1407,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 232,
+            "execution_count": 307,
             "metadata": {},
             "outputs": [
                 {
@@ -1473,7 +1481,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 233,
+            "execution_count": 308,
             "metadata": {},
             "outputs": [
                 {
@@ -1534,7 +1542,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 234,
+            "execution_count": 309,
             "metadata": {},
             "outputs": [
                 {
@@ -1560,7 +1568,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 235,
+            "execution_count": 310,
             "metadata": {},
             "outputs": [
                 {
@@ -1593,7 +1601,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 236,
+            "execution_count": 311,
             "metadata": {},
             "outputs": [
                 {
@@ -1629,7 +1637,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 237,
+            "execution_count": 312,
             "metadata": {},
             "outputs": [
                 {
@@ -1662,7 +1670,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 238,
+            "execution_count": 313,
             "metadata": {},
             "outputs": [
                 {
@@ -1693,7 +1701,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 239,
+            "execution_count": 314,
             "metadata": {},
             "outputs": [
                 {
@@ -1719,7 +1727,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 240,
+            "execution_count": 315,
             "metadata": {},
             "outputs": [
                 {
@@ -1751,7 +1759,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 241,
+            "execution_count": 316,
             "metadata": {},
             "outputs": [
                 {
@@ -1795,7 +1803,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 242,
+            "execution_count": 317,
             "metadata": {},
             "outputs": [
                 {
@@ -1829,7 +1837,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 243,
+            "execution_count": 318,
             "metadata": {},
             "outputs": [
                 {
@@ -1862,7 +1870,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 244,
+            "execution_count": 319,
             "metadata": {},
             "outputs": [
                 {
@@ -1915,7 +1923,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 245,
+            "execution_count": 320,
             "metadata": {},
             "outputs": [
                 {
@@ -1923,15 +1931,21 @@
                     "output_type": "stream",
                     "text": [
                         "Norms:\n",
-                        "tensor(6.6564)\n",
-                        "tensor(6.6564)\n",
+                        "tensor(2.6091)\n",
+                        "tensor(2.6091)\n",
                         "Right dimension: 5, Left dimension: 5, Hidden dimension: 2\n"
                     ]
                 }
             ],
             "source": [
-                "A = torch.randn(5, 2)\n",
-                "B = torch.randn(2, 5)\n",
+                "if not IN_GITHUB:\n",
+                "    A = torch.randn(5, 2)\n",
+                "    B = torch.randn(2, 5)\n",
+                "else:\n",
+                "    numbers = torch.arange(0, 1, 1 / 10)\n",
+                "    A = numbers.reshape((5, 2))\n",
+                "    B = numbers.reshape((2, 5))\n",
+                "\n",
                 "AB = A @ B\n",
                 "AB_factor = FactoredMatrix(A, B)\n",
                 "print(\"Norms:\")\n",
@@ -1950,7 +1964,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 246,
+            "execution_count": 321,
             "metadata": {},
             "outputs": [
                 {
@@ -1958,13 +1972,14 @@
                     "output_type": "stream",
                     "text": [
                         "Eigenvalues:\n",
-                        "tensor([ 1.0657e-07+0.0000j,  1.8773e+00+1.4089j,  1.8773e+00-1.4089j,\n",
-                        "        -2.1421e-08+0.0000j,  9.2447e-08+0.0000j])\n",
-                        "tensor([1.8773+1.4089j, 1.8773-1.4089j])\n",
+                        "tensor([ 2.5302e+00+0.0000e+00j,  1.9761e-02+0.0000e+00j,\n",
+                        "        -2.4053e-08+0.0000e+00j,  1.2246e-08+1.4947e-08j,\n",
+                        "         1.2246e-08-1.4947e-08j])\n",
+                        "tensor([0.0198+0.j, 2.5302+0.j])\n",
                         "\n",
                         "Singular Values:\n",
-                        "tensor([6.1567e+00, 2.5303e+00, 2.5031e-07, 3.7791e-08, 4.8972e-09])\n",
-                        "tensor([6.1567, 2.5303])\n"
+                        "tensor([2.6090e+00, 1.9164e-02, 7.1498e-08, 2.1756e-08, 1.4300e-08])\n",
+                        "tensor([2.6090, 0.0192])\n"
                     ]
                 }
             ],
@@ -1987,21 +2002,26 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 247,
+            "execution_count": 322,
             "metadata": {},
             "outputs": [
                 {
                     "name": "stdout",
                     "output_type": "stream",
                     "text": [
-                        "Unfactored: torch.Size([5, 300]) tensor(118.8196)\n",
-                        "Factored: torch.Size([5, 300]) tensor(118.8196)\n",
+                        "Unfactored: torch.Size([5, 300]) tensor(56.8577)\n",
+                        "Factored: torch.Size([5, 300]) tensor(56.8577)\n",
                         "Right dimension: 300, Left dimension: 5, Hidden dimension: 2\n"
                     ]
                 }
             ],
             "source": [
-                "C = torch.randn(5, 300)\n",
+                "if not IN_GITHUB:\n",
+                "    C = torch.randn(5, 300)\n",
+                "else:\n",
+                "    numbers = torch.arange(0, 1, 1/ 1500)\n",
+                "    C = numbers.reshape((5, 300))\n",
+                "\n",
                 "ABC = AB @ C\n",
                 "ABC_factor = AB_factor @ C\n",
                 "print(\"Unfactored:\", ABC.shape, ABC.norm())\n",
@@ -2018,7 +2038,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 248,
+            "execution_count": 323,
             "metadata": {},
             "outputs": [
                 {
@@ -2061,7 +2081,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 249,
+            "execution_count": 324,
             "metadata": {},
             "outputs": [
                 {
@@ -2079,7 +2099,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 250,
+            "execution_count": 325,
             "metadata": {},
             "outputs": [
                 {
@@ -2099,7 +2119,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 251,
+            "execution_count": 326,
             "metadata": {},
             "outputs": [
                 {
@@ -2109,9 +2129,9 @@
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                             "<body>\n",
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+            "execution_count": 327,
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@@ -2356,7 +2376,7 @@
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             "cell_type": "code",
-            "execution_count": 257,
+            "execution_count": 332,
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             "outputs": [
                 {
@@ -2393,13 +2413,13 @@
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+            "execution_count": 333,
             "metadata": {},
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                 {
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                         "application/vnd.jupyter.widget-view+json": {
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+                            "model_id": "b5ce142f71a6404d94598dc728039a1f",
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@@ -2413,10 +2433,10 @@
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                         "text/plain": [
-                            "'(CNN) President Barack Obama caught in embarrassing new scandal\\n\\nThe president\\'s infamous \"political adviser\" is now suing the Republican National Committee for the alleged negligence of his wife, accusing the group of \"trying to make her look bad.\"\\n\\nThe lawsuit, filed on Thursday in federal court in Washington'"
+                            "'(CNN) President Barack Obama caught in embarrassing new scandal\\n\\nThe White House said Thursday it has received a letter from the Office of Government Ethics and will cooperate fully with it.\\n\\nA letter from the Office of Government Ethics and its advocacy office, Public Counsel, warned the White House that a White House'"
                         ]
                     },
-                    "execution_count": 258,
+                    "execution_count": 333,
                     "metadata": {},
                     "output_type": "execute_result"
                 }
@@ -2471,7 +2491,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 259,
+            "execution_count": 334,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -2529,7 +2549,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 260,
+            "execution_count": 335,
             "metadata": {},
             "outputs": [
                 {
@@ -2564,7 +2584,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 261,
+            "execution_count": 336,
             "metadata": {},
             "outputs": [
                 {
@@ -2631,7 +2651,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 262,
+            "execution_count": 337,
             "metadata": {},
             "outputs": [
                 {
@@ -2641,9 +2661,9 @@
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@@ -2871,7 +2891,7 @@
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@@ -2894,7 +2914,7 @@
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                             "                            \n",
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From 459c8810cbd2533dec3844fc2b0b097bb5532afa Mon Sep 17 00:00:00 2001
From: Bryce Meyer <bryce13950@gmail.com>
Date: Mon, 23 Oct 2023 19:59:45 +0200
Subject: [PATCH 16/25] regenerated demo content with more expanded random
 range

---
 demos/Main_Demo.ipynb | 234 +++++++++++++++++++++---------------------
 1 file changed, 117 insertions(+), 117 deletions(-)

diff --git a/demos/Main_Demo.ipynb b/demos/Main_Demo.ipynb
index f24e2dc50..bc86d4b18 100644
--- a/demos/Main_Demo.ipynb
+++ b/demos/Main_Demo.ipynb
@@ -45,7 +45,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 272,
+            "execution_count": 372,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -81,7 +81,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 273,
+            "execution_count": 373,
             "metadata": {},
             "outputs": [
                 {
@@ -104,27 +104,27 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 274,
+            "execution_count": 374,
             "metadata": {},
             "outputs": [
                 {
                     "data": {
                         "text/html": [
-                            "<div id=\"circuits-vis-79a78105-f1c8\" style=\"margin: 15px 0;\"/>\n",
+                            "<div id=\"circuits-vis-c28642ae-d375\" style=\"margin: 15px 0;\"/>\n",
                             "    <script crossorigin type=\"module\">\n",
                             "    import { render, Hello } from \"https://unpkg.com/circuitsvis@1.43.0/dist/cdn/esm.js\";\n",
                             "    render(\n",
-                            "      \"circuits-vis-79a78105-f1c8\",\n",
+                            "      \"circuits-vis-c28642ae-d375\",\n",
                             "      Hello,\n",
                             "      {\"name\": \"Neel\"}\n",
                             "    )\n",
                             "    </script>"
                         ],
                         "text/plain": [
-                            "<circuitsvis.utils.render.RenderedHTML at 0xffff144540d0>"
+                            "<circuitsvis.utils.render.RenderedHTML at 0xffff1469abe0>"
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+                    "execution_count": 374,
                     "metadata": {
                         "text/html": {
                             "Content-Type": "text/html"
@@ -141,7 +141,7 @@
         },
         {
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-            "execution_count": 275,
+            "execution_count": 375,
             "metadata": {},
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@@ -173,7 +173,7 @@
         },
         {
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-            "execution_count": 276,
+            "execution_count": 376,
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@@ -195,16 +195,16 @@
         },
         {
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-            "execution_count": 277,
+            "execution_count": 377,
             "metadata": {},
             "outputs": [
                 {
                     "data": {
                         "text/plain": [
-                            "<torch.autograd.grad_mode.set_grad_enabled at 0xffff144dfca0>"
+                            "<torch.autograd.grad_mode.set_grad_enabled at 0xffff2d0402e0>"
                         ]
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-                    "execution_count": 277,
+                    "execution_count": 377,
                     "metadata": {},
                     "output_type": "execute_result"
                 }
@@ -222,7 +222,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 278,
+            "execution_count": 378,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -270,7 +270,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 279,
+            "execution_count": 379,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -279,7 +279,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 280,
+            "execution_count": 380,
             "metadata": {},
             "outputs": [
                 {
@@ -316,7 +316,7 @@
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         {
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-            "execution_count": 281,
+            "execution_count": 381,
             "metadata": {},
             "outputs": [
                 {
@@ -354,7 +354,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 282,
+            "execution_count": 382,
             "metadata": {},
             "outputs": [
                 {
@@ -388,7 +388,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 283,
+            "execution_count": 383,
             "metadata": {},
             "outputs": [
                 {
@@ -409,7 +409,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 284,
+            "execution_count": 384,
             "metadata": {},
             "outputs": [
                 {
@@ -422,21 +422,21 @@
                 {
                     "data": {
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-                            "<div id=\"circuits-vis-02c37ec0-7179\" style=\"margin: 15px 0;\"/>\n",
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                             "    <script crossorigin type=\"module\">\n",
                             "    import { render, AttentionPatterns } from \"https://unpkg.com/circuitsvis@1.43.0/dist/cdn/esm.js\";\n",
                             "    render(\n",
-                            "      \"circuits-vis-02c37ec0-7179\",\n",
+                            "      \"circuits-vis-129dc842-047b\",\n",
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@@ -618,13 +618,13 @@
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@@ -678,7 +678,7 @@
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                             "                            \n",
-                            "var gd = document.getElementById('8a5790b0-b15d-419b-a1d8-3d887c9b0f45');\n",
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@@ -928,24 +928,24 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 300,
+            "execution_count": 391,
             "metadata": {},
             "outputs": [
                 {
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@@ -1016,7 +1016,7 @@
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+            "execution_count": 392,
             "metadata": {},
             "outputs": [
                 {
@@ -1041,7 +1041,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 302,
+            "execution_count": 393,
             "metadata": {},
             "outputs": [
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                             "                            \n",
-                            "var gd = document.getElementById('f4333c94-b236-46ab-949c-e61a3b8b4640');\n",
+                            "var gd = document.getElementById('acea392b-5b0a-419e-93fd-9eeadef638cd');\n",
                             "var x = new MutationObserver(function (mutations, observer) {{\n",
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@@ -1232,7 +1232,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 303,
+            "execution_count": 394,
             "metadata": {},
             "outputs": [
                 {
@@ -1269,7 +1269,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 304,
+            "execution_count": 395,
             "metadata": {},
             "outputs": [
                 {
@@ -1307,7 +1307,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 305,
+            "execution_count": 396,
             "metadata": {},
             "outputs": [
                 {
@@ -1397,7 +1397,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 306,
+            "execution_count": 397,
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             "source": [
@@ -1407,7 +1407,7 @@
         },
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             "cell_type": "code",
-            "execution_count": 307,
+            "execution_count": 398,
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                 {
@@ -1481,7 +1481,7 @@
         },
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                 {
@@ -1542,7 +1542,7 @@
         },
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             "cell_type": "code",
-            "execution_count": 309,
+            "execution_count": 400,
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@@ -1568,7 +1568,7 @@
         },
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             "cell_type": "code",
-            "execution_count": 310,
+            "execution_count": 401,
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@@ -1601,7 +1601,7 @@
         },
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             "cell_type": "code",
-            "execution_count": 311,
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@@ -1637,7 +1637,7 @@
         },
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             "cell_type": "code",
-            "execution_count": 312,
+            "execution_count": 403,
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                 {
@@ -1670,7 +1670,7 @@
         },
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@@ -1701,7 +1701,7 @@
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-            "execution_count": 314,
+            "execution_count": 405,
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@@ -1727,7 +1727,7 @@
         },
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-            "execution_count": 315,
+            "execution_count": 406,
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@@ -1759,7 +1759,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 316,
+            "execution_count": 407,
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                 {
@@ -1803,7 +1803,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 317,
+            "execution_count": 408,
             "metadata": {},
             "outputs": [
                 {
@@ -1837,7 +1837,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 318,
+            "execution_count": 409,
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                 {
@@ -1870,7 +1870,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 319,
+            "execution_count": 410,
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             "outputs": [
                 {
@@ -1923,7 +1923,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 320,
+            "execution_count": 411,
             "metadata": {},
             "outputs": [
                 {
@@ -1931,8 +1931,8 @@
                     "output_type": "stream",
                     "text": [
                         "Norms:\n",
-                        "tensor(2.6091)\n",
-                        "tensor(2.6091)\n",
+                        "tensor(7.2883)\n",
+                        "tensor(7.2883)\n",
                         "Right dimension: 5, Left dimension: 5, Hidden dimension: 2\n"
                     ]
                 }
@@ -1942,7 +1942,7 @@
                 "    A = torch.randn(5, 2)\n",
                 "    B = torch.randn(2, 5)\n",
                 "else:\n",
-                "    numbers = torch.arange(0, 1, 1 / 10)\n",
+                "    numbers = torch.arange(-2, 2, 4 / 10)\n",
                 "    A = numbers.reshape((5, 2))\n",
                 "    B = numbers.reshape((2, 5))\n",
                 "\n",
@@ -1964,7 +1964,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 321,
+            "execution_count": 412,
             "metadata": {},
             "outputs": [
                 {
@@ -1972,14 +1972,12 @@
                     "output_type": "stream",
                     "text": [
                         "Eigenvalues:\n",
-                        "tensor([ 2.5302e+00+0.0000e+00j,  1.9761e-02+0.0000e+00j,\n",
-                        "        -2.4053e-08+0.0000e+00j,  1.2246e-08+1.4947e-08j,\n",
-                        "         1.2246e-08-1.4947e-08j])\n",
-                        "tensor([0.0198+0.j, 2.5302+0.j])\n",
+                        "tensor([6.9613e+00+0.j, 2.6937e-07+0.j, 1.8388e+00+0.j, 8.0792e-08+0.j, 1.7887e-08+0.j])\n",
+                        "tensor([6.9612+0.j, 1.8388+0.j])\n",
                         "\n",
                         "Singular Values:\n",
-                        "tensor([2.6090e+00, 1.9164e-02, 7.1498e-08, 2.1756e-08, 1.4300e-08])\n",
-                        "tensor([2.6090, 0.0192])\n"
+                        "tensor([7.0592e+00, 1.8132e+00, 4.1223e-07, 5.0490e-08, 8.5499e-12])\n",
+                        "tensor([7.0592, 1.8132])\n"
                     ]
                 }
             ],
@@ -2002,24 +2000,25 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 322,
+            "execution_count": 413,
             "metadata": {},
             "outputs": [
                 {
                     "name": "stdout",
                     "output_type": "stream",
                     "text": [
-                        "Unfactored: torch.Size([5, 300]) tensor(56.8577)\n",
-                        "Factored: torch.Size([5, 300]) tensor(56.8577)\n",
+                        "Unfactored: torch.Size([5, 300]) tensor(286.4420)\n",
+                        "Factored: torch.Size([5, 300]) tensor(286.4420)\n",
                         "Right dimension: 300, Left dimension: 5, Hidden dimension: 2\n"
                     ]
                 }
             ],
             "source": [
+                "IN_GITHUB = True\n",
                 "if not IN_GITHUB:\n",
                 "    C = torch.randn(5, 300)\n",
                 "else:\n",
-                "    numbers = torch.arange(0, 1, 1/ 1500)\n",
+                "    numbers = torch.arange(-2, 2, 4 / 1500)\n",
                 "    C = numbers.reshape((5, 300))\n",
                 "\n",
                 "ABC = AB @ C\n",
@@ -2038,7 +2037,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 323,
+            "execution_count": 414,
             "metadata": {},
             "outputs": [
                 {
@@ -2081,7 +2080,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 324,
+            "execution_count": 415,
             "metadata": {},
             "outputs": [
                 {
@@ -2099,7 +2098,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 325,
+            "execution_count": 416,
             "metadata": {},
             "outputs": [
                 {
@@ -2119,7 +2118,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 326,
+            "execution_count": 417,
             "metadata": {},
             "outputs": [
                 {
@@ -2129,9 +2128,9 @@
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@@ -2176,7 +2175,7 @@
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+            "execution_count": 418,
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                             "                            \n",
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@@ -2376,7 +2375,7 @@
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         {
             "cell_type": "code",
-            "execution_count": 332,
+            "execution_count": 423,
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             "outputs": [
                 {
@@ -2413,13 +2412,13 @@
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+            "execution_count": 424,
             "metadata": {},
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                 {
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                         "application/vnd.jupyter.widget-view+json": {
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+                            "model_id": "64362cab0a17441d86d585c611f953ba",
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                     "data": {
                         "text/plain": [
-                            "'(CNN) President Barack Obama caught in embarrassing new scandal\\n\\nThe White House said Thursday it has received a letter from the Office of Government Ethics and will cooperate fully with it.\\n\\nA letter from the Office of Government Ethics and its advocacy office, Public Counsel, warned the White House that a White House'"
+                            "'(CNN) President Barack Obama caught in embarrassing new scandal\\n\\nA former White House aide named Hamid Ansari was charged with money laundering and ordered to pay restitution to the IRS.\\n\\nA federal civil case is still pending, and Ansari reportedly is being held in federal prison on a $1 million'"
                         ]
                     },
-                    "execution_count": 333,
+                    "execution_count": 424,
                     "metadata": {},
                     "output_type": "execute_result"
                 }
             ],
             "source": [
+                "# NBVAL_IGNORE_OUTPUT\n",
                 "model.generate(\"(CNN) President Barack Obama caught in embarrassing new scandal\\n\", max_new_tokens=50, temperature=0.7, prepend_bos=True)"
             ]
         },
@@ -2491,7 +2491,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 334,
+            "execution_count": 425,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -2549,7 +2549,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 335,
+            "execution_count": 426,
             "metadata": {},
             "outputs": [
                 {
@@ -2584,7 +2584,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 336,
+            "execution_count": 427,
             "metadata": {},
             "outputs": [
                 {
@@ -2651,7 +2651,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 337,
+            "execution_count": 428,
             "metadata": {},
             "outputs": [
                 {
@@ -2661,9 +2661,9 @@
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                             "                            \n",
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@@ -2914,7 +2914,7 @@
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On=%{x}\\u003cbr\\u003eindex=%{y}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"\",\"line\":{\"color\":\"#636efa\",\"dash\":\"solid\"},\"marker\":{\"symbol\":\"circle\"},\"mode\":\"lines+markers\",\"name\":\"\",\"orientation\":\"h\",\"showlegend\":false,\"x\":[22282240,187432960,528482304,1628438528,21780496384],\"xaxis\":\"x\",\"y\":[0,1,2,3,4],\"yaxis\":\"y\",\"type\":\"scatter\"}],                        {\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Tokens Trained On\"},\"type\":\"log\"},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"index\"}},\"legend\":{\"tracegroupgap\":0},\"title\":{\"text\":\"Induction Loss over training: solu-2l\"}},                        {\"responsive\": true}                    ).then(function(){\n",
                             "                            \n",
-                            "var gd = document.getElementById('29e5c63d-7b30-42b3-995a-d6590a76efa0');\n",
+                            "var gd = document.getElementById('9f281bbc-67fb-4004-a1fa-d636e3a913b7');\n",
                             "var x = new MutationObserver(function (mutations, observer) {{\n",
                             "        var display = window.getComputedStyle(gd).display;\n",
                             "        if (!display || display === 'none') {{\n",

From 9e43db5dfeb78c33a7453aa72b760df25aac7107 Mon Sep 17 00:00:00 2001
From: Bryce Meyer <bryce13950@gmail.com>
Date: Mon, 23 Oct 2023 20:08:10 +0200
Subject: [PATCH 17/25] aded regex to replace all white space

---
 demos/doc_sanitize.cfg | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/demos/doc_sanitize.cfg b/demos/doc_sanitize.cfg
index c21ea942f..7f5040e49 100644
--- a/demos/doc_sanitize.cfg
+++ b/demos/doc_sanitize.cfg
@@ -8,4 +8,8 @@ replace: TIME-STAMP
 
 [regex3]
 regex: 0[xX][0-9a-fA-F]+
-replace: HEX-CODE
\ No newline at end of file
+replace: HEX-CODE
+
+[regex4]
+regex: [\n\r\s]+
+replace: ""
\ No newline at end of file

From 87e27deeba9c0d57558711c756d5a20b6601b114 Mon Sep 17 00:00:00 2001
From: Bryce Meyer <bryce13950@gmail.com>
Date: Mon, 23 Oct 2023 20:38:07 +0200
Subject: [PATCH 18/25] marked last few cells as skip output

---
 demos/Main_Demo.ipynb  | 2 ++
 demos/doc_sanitize.cfg | 6 +-----
 makefile               | 4 ++--
 3 files changed, 5 insertions(+), 7 deletions(-)

diff --git a/demos/Main_Demo.ipynb b/demos/Main_Demo.ipynb
index bc86d4b18..d29dcbe29 100644
--- a/demos/Main_Demo.ipynb
+++ b/demos/Main_Demo.ipynb
@@ -1982,6 +1982,7 @@
                 }
             ],
             "source": [
+                "# NBVAL_IGNORE_OUTPUT\n",
                 "print(\"Eigenvalues:\")\n",
                 "print(torch.linalg.eig(AB).eigenvalues)\n",
                 "print(AB_factor.eigenvalues)\n",
@@ -2930,6 +2931,7 @@
                 }
             ],
             "source": [
+                "# NBVAL_IGNORE_OUTPUT\n",
                 "for index in checkpoint_indices:\n",
                 "    # Load the model from the relevant checkpoint by index\n",
                 "    model_for_this_checkpoint = HookedTransformer.from_pretrained(model_name, checkpoint_index=index, device=device)\n",
diff --git a/demos/doc_sanitize.cfg b/demos/doc_sanitize.cfg
index 7f5040e49..c21ea942f 100644
--- a/demos/doc_sanitize.cfg
+++ b/demos/doc_sanitize.cfg
@@ -8,8 +8,4 @@ replace: TIME-STAMP
 
 [regex3]
 regex: 0[xX][0-9a-fA-F]+
-replace: HEX-CODE
-
-[regex4]
-regex: [\n\r\s]+
-replace: ""
\ No newline at end of file
+replace: HEX-CODE
\ No newline at end of file
diff --git a/makefile b/makefile
index 96f4fa0d1..57696d6e3 100644
--- a/makefile
+++ b/makefile
@@ -18,8 +18,8 @@ docstring-test:
 	poetry run pytest transformer_lens/
 
 notebook-test:
-	poetry run pytest --sanitize-with demos/doc_sanitize.cfg demos/Main_Demo.ipynb 
-	poetry run pytest --sanitize-with demos/doc_sanitize.cfg demos/Exploratory_Analysis_Demo.ipynb
+	poetry run pytest --nbval-sanitize-with demos/doc_sanitize.cfg demos/Main_Demo.ipynb 
+	poetry run pytest --nbval-sanitize-with demos/doc_sanitize.cfg demos/Exploratory_Analysis_Demo.ipynb
 
 test:
 	make unit-test

From 5f2e6fde57bc05c07f13b05292a672850e19fa88 Mon Sep 17 00:00:00 2001
From: Bryce Meyer <bryce13950@gmail.com>
Date: Mon, 23 Oct 2023 21:14:10 +0200
Subject: [PATCH 19/25] turned off error warnings

---
 docs/make_docs.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/make_docs.py b/docs/make_docs.py
index ea6c41fba..c6b17f09d 100644
--- a/docs/make_docs.py
+++ b/docs/make_docs.py
@@ -141,7 +141,7 @@ def build_docs():
             SOURCE_PATH,
             BUILD_PATH,
             # "-n",  # Nitpicky mode (warn about all missing references)
-            "-W",  # Turn warnings into errors
+            # "-W",  # Turn warnings into errors
         ],
         check=True,
     )

From 234c1e85fb345e3b2adb0f51e98e348f2d4eb7e2 Mon Sep 17 00:00:00 2001
From: Bryce Meyer <bryce13950@gmail.com>
Date: Mon, 23 Oct 2023 21:27:13 +0200
Subject: [PATCH 20/25] added warn to error flag again

---
 docs/make_docs.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/make_docs.py b/docs/make_docs.py
index c6b17f09d..ea6c41fba 100644
--- a/docs/make_docs.py
+++ b/docs/make_docs.py
@@ -141,7 +141,7 @@ def build_docs():
             SOURCE_PATH,
             BUILD_PATH,
             # "-n",  # Nitpicky mode (warn about all missing references)
-            # "-W",  # Turn warnings into errors
+            "-W",  # Turn warnings into errors
         ],
         check=True,
     )

From 591bd36946c36bb9f6888553c0fe7e3a417ce80c Mon Sep 17 00:00:00 2001
From: Bryce Meyer <bryce13950@gmail.com>
Date: Thu, 26 Oct 2023 23:13:43 +0200
Subject: [PATCH 21/25] revised demo

---
 demos/Main_Demo.ipynb | 335 ++++++++++++++---------------
 poetry.lock           | 474 +-----------------------------------------
 pyproject.toml        |   2 +-
 3 files changed, 154 insertions(+), 657 deletions(-)

diff --git a/demos/Main_Demo.ipynb b/demos/Main_Demo.ipynb
index d29dcbe29..f17e7dec7 100644
--- a/demos/Main_Demo.ipynb
+++ b/demos/Main_Demo.ipynb
@@ -45,7 +45,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 372,
+            "execution_count": 158,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -81,7 +81,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 373,
+            "execution_count": 159,
             "metadata": {},
             "outputs": [
                 {
@@ -104,27 +104,27 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 374,
+            "execution_count": 160,
             "metadata": {},
             "outputs": [
                 {
                     "data": {
                         "text/html": [
-                            "<div id=\"circuits-vis-c28642ae-d375\" style=\"margin: 15px 0;\"/>\n",
+                            "<div id=\"circuits-vis-1e971456-b1dc\" style=\"margin: 15px 0;\"/>\n",
                             "    <script crossorigin type=\"module\">\n",
                             "    import { render, Hello } from \"https://unpkg.com/circuitsvis@1.43.0/dist/cdn/esm.js\";\n",
                             "    render(\n",
-                            "      \"circuits-vis-c28642ae-d375\",\n",
+                            "      \"circuits-vis-1e971456-b1dc\",\n",
                             "      Hello,\n",
                             "      {\"name\": \"Neel\"}\n",
                             "    )\n",
                             "    </script>"
                         ],
                         "text/plain": [
-                            "<circuitsvis.utils.render.RenderedHTML at 0xffff1469abe0>"
+                            "<circuitsvis.utils.render.RenderedHTML at 0xffff1014d280>"
                         ]
                     },
-                    "execution_count": 374,
+                    "execution_count": 160,
                     "metadata": {
                         "text/html": {
                             "Content-Type": "text/html"
@@ -141,49 +141,34 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 375,
+            "execution_count": 161,
             "metadata": {},
             "outputs": [],
             "source": [
                 "# Import stuff\n",
                 "import torch\n",
                 "import torch.nn as nn\n",
-                "import torch.nn.functional as F\n",
-                "import torch.optim as optim\n",
-                "import numpy as np\n",
                 "import einops\n",
                 "from fancy_einsum import einsum\n",
                 "import tqdm.auto as tqdm\n",
-                "import random\n",
-                "from pathlib import Path\n",
                 "import plotly.express as px\n",
-                "from torch.utils.data import DataLoader\n",
                 "\n",
-                "from jaxtyping import Float, Int\n",
-                "from typing import List, Union, Optional\n",
-                "from functools import partial\n",
-                "import copy\n",
-                "\n",
-                "import itertools\n",
-                "from transformers import AutoModelForCausalLM, AutoConfig, AutoTokenizer\n",
-                "import dataclasses\n",
-                "import datasets\n",
-                "from IPython.display import HTML"
+                "from jaxtyping import Float\n",
+                "from functools import partial"
             ]
         },
         {
             "cell_type": "code",
-            "execution_count": 376,
+            "execution_count": 162,
             "metadata": {},
             "outputs": [],
             "source": [
-                "import transformer_lens\n",
+                "# import transformer_lens\n",
                 "import transformer_lens.utils as utils\n",
                 "from transformer_lens.hook_points import (\n",
-                "    HookedRootModule,\n",
                 "    HookPoint,\n",
                 ")  # Hooking utilities\n",
-                "from transformer_lens import HookedTransformer, HookedTransformerConfig, FactoredMatrix, ActivationCache"
+                "from transformer_lens import HookedTransformer, FactoredMatrix"
             ]
         },
         {
@@ -195,16 +180,16 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 377,
+            "execution_count": 163,
             "metadata": {},
             "outputs": [
                 {
                     "data": {
                         "text/plain": [
-                            "<torch.autograd.grad_mode.set_grad_enabled at 0xffff2d0402e0>"
+                            "<torch.autograd.grad_mode.set_grad_enabled at 0xffff037d2460>"
                         ]
                     },
-                    "execution_count": 377,
+                    "execution_count": 163,
                     "metadata": {},
                     "output_type": "execute_result"
                 }
@@ -222,7 +207,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 378,
+            "execution_count": 164,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -270,16 +255,16 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 379,
+            "execution_count": 165,
             "metadata": {},
             "outputs": [],
             "source": [
-                "device = \"cuda\" if torch.cuda.is_available() else \"cpu\""
+                "device = utils.get_device()"
             ]
         },
         {
             "cell_type": "code",
-            "execution_count": 380,
+            "execution_count": 166,
             "metadata": {},
             "outputs": [
                 {
@@ -316,7 +301,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 381,
+            "execution_count": 167,
             "metadata": {},
             "outputs": [
                 {
@@ -354,7 +339,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 382,
+            "execution_count": 168,
             "metadata": {},
             "outputs": [
                 {
@@ -388,7 +373,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 383,
+            "execution_count": 169,
             "metadata": {},
             "outputs": [
                 {
@@ -409,7 +394,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 384,
+            "execution_count": 170,
             "metadata": {},
             "outputs": [
                 {
@@ -422,21 +407,21 @@
                 {
                     "data": {
                         "text/html": [
-                            "<div id=\"circuits-vis-129dc842-047b\" style=\"margin: 15px 0;\"/>\n",
+                            "<div id=\"circuits-vis-c465a148-407d\" style=\"margin: 15px 0;\"/>\n",
                             "    <script crossorigin type=\"module\">\n",
                             "    import { render, AttentionPatterns } from \"https://unpkg.com/circuitsvis@1.43.0/dist/cdn/esm.js\";\n",
                             "    render(\n",
-                            "      \"circuits-vis-129dc842-047b\",\n",
+                            "      \"circuits-vis-c465a148-407d\",\n",
                             "      AttentionPatterns,\n",
                             "      {\"tokens\": [\"<|endoftext|>\", \"Natural\", \" language\", \" processing\", \" tasks\", \",\", \" such\", \" as\", \" question\", \" answering\", \",\", \" machine\", \" translation\", \",\", \" reading\", \" comprehension\", \",\", \" and\", \" summar\", \"ization\", \",\", \" are\", \" typically\", \" approached\", \" with\", \" supervised\", \" learning\", \" on\", \" tasks\", \"pe\", \"cific\", \" datasets\", \".\"], \"attention\": [[[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.9639418125152588, 0.03605816140770912, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.8389372825622559, 0.11828784644603729, 0.042774833738803864, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 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@@ -618,13 +603,13 @@
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@@ -678,7 +663,7 @@
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                             "                            \n",
-                            "var gd = document.getElementById('871a89d6-01eb-42ab-88af-b9987c4969dc');\n",
+                            "var gd = document.getElementById('48af2906-9da3-4275-a009-6b75b74f2011');\n",
                             "var x = new MutationObserver(function (mutations, observer) {{\n",
                             "        var display = window.getComputedStyle(gd).display;\n",
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@@ -812,11 +797,7 @@
                 "batch_size = 10\n",
                 "seq_len = 50\n",
                 "size = (batch_size, seq_len)\n",
-                "if not IN_GITHUB:\n",
-                "    input_tensor = torch.randint(1000, 10000, size)\n",
-                "else: \n",
-                "    numbers = torch.arange(1, 501, 1)\n",
-                "    input_tensor = numbers.reshape(size)\n",
+                "input_tensor = torch.randint(1000, 10000, size)\n",
                 "\n",
                 "random_tokens = input_tensor.to(model.cfg.device)\n",
                 "repeated_tokens = einops.repeat(random_tokens, \"batch seq_len -> batch (2 seq_len)\")\n",
@@ -844,7 +825,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 390,
+            "execution_count": 176,
             "metadata": {},
             "outputs": [
                 {
@@ -854,9 +835,9 @@
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                             "                            \n",
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@@ -928,24 +909,24 @@
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-            "execution_count": 391,
+            "execution_count": 177,
             "metadata": {},
             "outputs": [
                 {
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                             "    )\n",
                             "    </script>"
                         ],
                         "text/plain": [
-                            "<circuitsvis.utils.render.RenderedHTML at 0xffff141647c0>"
+                            "<circuitsvis.utils.render.RenderedHTML at 0xffff10fadf40>"
                         ]
                     },
                     "metadata": {
@@ -957,14 +938,13 @@
                 }
             ],
             "source": [
+                "if IN_GITHUB:\n",
+                "    torch.manual_seed(50)\n",
+                "    \n",
                 "induction_head_layer = 5\n",
                 "induction_head_index = 5\n",
                 "size = (1, 20)\n",
-                "if not IN_GITHUB:\n",
-                "    input_tensor = torch.randint(1000, 10000, size)\n",
-                "else: \n",
-                "    numbers = torch.arange(1, 501, 500/20, dtype=torch.int)\n",
-                "    input_tensor = numbers.reshape(size)\n",
+                "input_tensor = torch.randint(1000, 10000, size)\n",
                 "\n",
                 "single_random_sequence = input_tensor.to(model.cfg.device)\n",
                 "repeated_random_sequence = einops.repeat(single_random_sequence, \"batch seq_len -> batch (2 seq_len)\")\n",
@@ -1016,7 +996,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 392,
+            "execution_count": 178,
             "metadata": {},
             "outputs": [
                 {
@@ -1041,7 +1021,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 393,
+            "execution_count": 179,
             "metadata": {},
             "outputs": [
                 {
@@ -1051,9 +1031,9 @@
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                             "                            \n",
-                            "var gd = document.getElementById('acea392b-5b0a-419e-93fd-9eeadef638cd');\n",
+                            "var gd = document.getElementById('bec758c8-b743-473a-bebd-04e732cee347');\n",
                             "var x = new MutationObserver(function (mutations, observer) {{\n",
                             "        var display = window.getComputedStyle(gd).display;\n",
                             "        if (!display || display === 'none') {{\n",
@@ -1232,7 +1212,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 394,
+            "execution_count": 180,
             "metadata": {},
             "outputs": [
                 {
@@ -1269,7 +1249,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 395,
+            "execution_count": 181,
             "metadata": {},
             "outputs": [
                 {
@@ -1307,7 +1287,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 396,
+            "execution_count": 182,
             "metadata": {},
             "outputs": [
                 {
@@ -1326,7 +1306,13 @@
                         "blocks.0.ln1.hook_normalized torch.Size([1, 10, 768])\n",
                         "blocks.0.attn.hook_q torch.Size([1, 10, 12, 64])\n",
                         "blocks.0.attn.hook_k torch.Size([1, 10, 12, 64])\n",
-                        "blocks.0.attn.hook_v torch.Size([1, 10, 12, 64])\n",
+                        "blocks.0.attn.hook_v torch.Size([1, 10, 12, 64])\n"
+                    ]
+                },
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
                         "blocks.0.attn.hook_attn_scores torch.Size([1, 12, 10, 10])\n",
                         "blocks.0.attn.hook_pattern torch.Size([1, 12, 10, 10])\n",
                         "blocks.0.attn.hook_z torch.Size([1, 10, 12, 64])\n",
@@ -1397,7 +1383,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 397,
+            "execution_count": 183,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -1407,7 +1393,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 398,
+            "execution_count": 184,
             "metadata": {},
             "outputs": [
                 {
@@ -1481,7 +1467,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 399,
+            "execution_count": 185,
             "metadata": {},
             "outputs": [
                 {
@@ -1542,7 +1528,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 400,
+            "execution_count": 186,
             "metadata": {},
             "outputs": [
                 {
@@ -1568,7 +1554,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 401,
+            "execution_count": 187,
             "metadata": {},
             "outputs": [
                 {
@@ -1601,7 +1587,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 402,
+            "execution_count": 188,
             "metadata": {},
             "outputs": [
                 {
@@ -1637,7 +1623,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 403,
+            "execution_count": 189,
             "metadata": {},
             "outputs": [
                 {
@@ -1670,7 +1656,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 404,
+            "execution_count": 190,
             "metadata": {},
             "outputs": [
                 {
@@ -1701,7 +1687,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 405,
+            "execution_count": 191,
             "metadata": {},
             "outputs": [
                 {
@@ -1727,7 +1713,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 406,
+            "execution_count": 192,
             "metadata": {},
             "outputs": [
                 {
@@ -1759,7 +1745,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 407,
+            "execution_count": 193,
             "metadata": {},
             "outputs": [
                 {
@@ -1803,7 +1789,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 408,
+            "execution_count": 194,
             "metadata": {},
             "outputs": [
                 {
@@ -1837,7 +1823,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 409,
+            "execution_count": 195,
             "metadata": {},
             "outputs": [
                 {
@@ -1870,7 +1856,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 410,
+            "execution_count": 196,
             "metadata": {},
             "outputs": [
                 {
@@ -1923,7 +1909,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 411,
+            "execution_count": 197,
             "metadata": {},
             "outputs": [
                 {
@@ -1931,20 +1917,17 @@
                     "output_type": "stream",
                     "text": [
                         "Norms:\n",
-                        "tensor(7.2883)\n",
-                        "tensor(7.2883)\n",
+                        "tensor(9.9105)\n",
+                        "tensor(9.9105)\n",
                         "Right dimension: 5, Left dimension: 5, Hidden dimension: 2\n"
                     ]
                 }
             ],
             "source": [
-                "if not IN_GITHUB:\n",
-                "    A = torch.randn(5, 2)\n",
-                "    B = torch.randn(2, 5)\n",
-                "else:\n",
-                "    numbers = torch.arange(-2, 2, 4 / 10)\n",
-                "    A = numbers.reshape((5, 2))\n",
-                "    B = numbers.reshape((2, 5))\n",
+                "if IN_GITHUB:\n",
+                "    torch.manual_seed(50)\n",
+                "A = torch.randn(5, 2)\n",
+                "B = torch.randn(2, 5)\n",
                 "\n",
                 "AB = A @ B\n",
                 "AB_factor = FactoredMatrix(A, B)\n",
@@ -1964,7 +1947,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 412,
+            "execution_count": 198,
             "metadata": {},
             "outputs": [
                 {
@@ -1972,17 +1955,17 @@
                     "output_type": "stream",
                     "text": [
                         "Eigenvalues:\n",
-                        "tensor([6.9613e+00+0.j, 2.6937e-07+0.j, 1.8388e+00+0.j, 8.0792e-08+0.j, 1.7887e-08+0.j])\n",
-                        "tensor([6.9612+0.j, 1.8388+0.j])\n",
+                        "tensor([-6.2877e+00+0.j,  1.9337e-07+0.j,  2.3121e+00+0.j, -5.9987e-07+0.j,\n",
+                        "        -1.1409e-07+0.j])\n",
+                        "tensor([-6.2877+0.j,  2.3121+0.j])\n",
                         "\n",
                         "Singular Values:\n",
-                        "tensor([7.0592e+00, 1.8132e+00, 4.1223e-07, 5.0490e-08, 8.5499e-12])\n",
-                        "tensor([7.0592, 1.8132])\n"
+                        "tensor([8.3126e+00, 5.3963e+00, 1.4519e-07, 7.4293e-08, 2.1726e-09])\n",
+                        "tensor([8.3126, 5.3963])\n"
                     ]
                 }
             ],
             "source": [
-                "# NBVAL_IGNORE_OUTPUT\n",
                 "print(\"Eigenvalues:\")\n",
                 "print(torch.linalg.eig(AB).eigenvalues)\n",
                 "print(AB_factor.eigenvalues)\n",
@@ -2001,31 +1984,29 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 413,
+            "execution_count": 199,
             "metadata": {},
             "outputs": [
                 {
                     "name": "stdout",
                     "output_type": "stream",
                     "text": [
-                        "Unfactored: torch.Size([5, 300]) tensor(286.4420)\n",
-                        "Factored: torch.Size([5, 300]) tensor(286.4420)\n",
+                        "Unfactored: torch.Size([5, 300]) tensor(160.0830)\n",
+                        "Factored: torch.Size([5, 300]) tensor(160.0830)\n",
                         "Right dimension: 300, Left dimension: 5, Hidden dimension: 2\n"
                     ]
                 }
             ],
             "source": [
-                "IN_GITHUB = True\n",
-                "if not IN_GITHUB:\n",
-                "    C = torch.randn(5, 300)\n",
-                "else:\n",
-                "    numbers = torch.arange(-2, 2, 4 / 1500)\n",
-                "    C = numbers.reshape((5, 300))\n",
+                "if IN_GITHUB:\n",
+                "    torch.manual_seed(50)\n",
+                "    \n",
+                "C = torch.randn(5, 300)\n",
                 "\n",
                 "ABC = AB @ C\n",
                 "ABC_factor = AB_factor @ C\n",
-                "print(\"Unfactored:\", ABC.shape, ABC.norm())\n",
-                "print(\"Factored:\", ABC_factor.shape, ABC_factor.norm())\n",
+                "print(\"Unfactored:\", ABC.shape, ABC.norm().round(decimals=3))\n",
+                "print(\"Factored:\", ABC_factor.shape, ABC_factor.norm().round(decimals=3))\n",
                 "print(f\"Right dimension: {ABC_factor.rdim}, Left dimension: {ABC_factor.ldim}, Hidden dimension: {ABC_factor.mdim}\")"
             ]
         },
@@ -2038,7 +2019,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 414,
+            "execution_count": 200,
             "metadata": {},
             "outputs": [
                 {
@@ -2081,7 +2062,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 415,
+            "execution_count": 201,
             "metadata": {},
             "outputs": [
                 {
@@ -2099,7 +2080,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 416,
+            "execution_count": 202,
             "metadata": {},
             "outputs": [
                 {
@@ -2119,7 +2100,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 417,
+            "execution_count": 203,
             "metadata": {},
             "outputs": [
                 {
@@ -2129,9 +2110,9 @@
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                             "<body>\n",
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@@ -2176,7 +2157,7 @@
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+            "execution_count": 204,
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@@ -2376,7 +2357,7 @@
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         {
             "cell_type": "code",
-            "execution_count": 423,
+            "execution_count": 209,
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                 {
@@ -2413,13 +2394,13 @@
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+            "execution_count": 210,
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                 {
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                         "application/vnd.jupyter.widget-view+json": {
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                         "text/plain": [
-                            "'(CNN) President Barack Obama caught in embarrassing new scandal\\n\\nA former White House aide named Hamid Ansari was charged with money laundering and ordered to pay restitution to the IRS.\\n\\nA federal civil case is still pending, and Ansari reportedly is being held in federal prison on a $1 million'"
+                            "'(CNN) President Barack Obama caught in embarrassing new scandal\\n\\nAmerican voters who backed Hillary Clinton gave President Barack Obama a 9.5-point lead over Republican Mitt Romney in the latest CNN/ORC International poll, his lowest level since the last CNN-ORC poll in 2006.\\n\\nRepublican voters'"
                         ]
                     },
-                    "execution_count": 424,
+                    "execution_count": 210,
                     "metadata": {},
                     "output_type": "execute_result"
                 }
@@ -2492,7 +2473,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 425,
+            "execution_count": 211,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -2550,7 +2531,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 426,
+            "execution_count": 212,
             "metadata": {},
             "outputs": [
                 {
@@ -2585,7 +2566,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 427,
+            "execution_count": 213,
             "metadata": {},
             "outputs": [
                 {
@@ -2652,7 +2633,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 428,
+            "execution_count": 214,
             "metadata": {},
             "outputs": [
                 {
@@ -2662,9 +2643,9 @@
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Values for solu-6l-pile (Linear scale)\"}},                        {\"responsive\": true}                    ).then(function(){\n",
                             "                            \n",
-                            "var gd = document.getElementById('751a1bf8-b817-4e7e-b7fd-c4b25d29c07f');\n",
+                            "var gd = document.getElementById('c6ff8351-5354-466f-91d0-22d43a4c3660');\n",
                             "var x = new MutationObserver(function (mutations, observer) {{\n",
                             "        var display = window.getComputedStyle(gd).display;\n",
                             "        if (!display || display === 'none') {{\n",
@@ -2892,7 +2873,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 429,
+            "execution_count": 215,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -2915,33 +2896,21 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 430,
+            "execution_count": 216,
             "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Loaded pretrained model solu-2l into HookedTransformer\n",
-                        "Loaded pretrained model solu-2l into HookedTransformer\n",
-                        "Loaded pretrained model solu-2l into HookedTransformer\n",
-                        "Loaded pretrained model solu-2l into HookedTransformer\n",
-                        "Loaded pretrained model solu-2l into HookedTransformer\n"
-                    ]
-                }
-            ],
+            "outputs": [],
             "source": [
-                "# NBVAL_IGNORE_OUTPUT\n",
-                "for index in checkpoint_indices:\n",
-                "    # Load the model from the relevant checkpoint by index\n",
-                "    model_for_this_checkpoint = HookedTransformer.from_pretrained(model_name, checkpoint_index=index, device=device)\n",
-                "    checkpointed_models.append(model_for_this_checkpoint)\n",
+                "if not IN_GITHUB:\n",
+                "    for index in checkpoint_indices:\n",
+                "        # Load the model from the relevant checkpoint by index\n",
+                "        model_for_this_checkpoint = HookedTransformer.from_pretrained(model_name, checkpoint_index=index, device=device)\n",
+                "        checkpointed_models.append(model_for_this_checkpoint)\n",
                 "\n",
-                "    tokens_seen_for_this_checkpoint = model_for_this_checkpoint.cfg.checkpoint_value\n",
-                "    tokens_trained_on.append(tokens_seen_for_this_checkpoint)\n",
+                "        tokens_seen_for_this_checkpoint = model_for_this_checkpoint.cfg.checkpoint_value\n",
+                "        tokens_trained_on.append(tokens_seen_for_this_checkpoint)\n",
                 "\n",
-                "    induction_loss_for_this_checkpoint = evals.induction_loss(model_for_this_checkpoint, device=device).item()\n",
-                "    induction_losses.append(induction_loss_for_this_checkpoint)"
+                "        induction_loss_for_this_checkpoint = evals.induction_loss(model_for_this_checkpoint, device=device).item()\n",
+                "        induction_losses.append(induction_loss_for_this_checkpoint)"
             ]
         },
         {
@@ -2955,7 +2924,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 431,
+            "execution_count": 217,
             "metadata": {},
             "outputs": [
                 {
@@ -2965,9 +2934,9 @@
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diff --git a/poetry.lock b/poetry.lock
index 2d7f3bb39..7ed367bff 100644
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 [[package]]
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-    {file = "Pillow-10.1.0.tar.gz", hash = "sha256:e6bf8de6c36ed96c86ea3b6e1d5273c53f46ef518a062464cd7ef5dd2cf92e38"},
-]
-
-[package.extras]
-docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-removed-in", "sphinxext-opengraph"]
-tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
-
 [[package]]
 name = "pkgutil-resolve-name"
 version = "1.3.10"
@@ -3531,20 +3073,6 @@ files = [
 [package.extras]
 plugins = ["importlib-metadata"]
 
-[[package]]
-name = "pyparsing"
-version = "3.1.1"
-description = "pyparsing module - Classes and methods to define and execute parsing grammars"
-optional = false
-python-versions = ">=3.6.8"
-files = [
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-    {file = "pyparsing-3.1.1.tar.gz", hash = "sha256:ede28a1a32462f5a9705e07aea48001a08f7cf81a021585011deba701581a0db"},
-]
-
-[package.extras]
-diagrams = ["jinja2", "railroad-diagrams"]
-
 [[package]]
 name = "pytest"
 version = "7.4.2"
@@ -5508,4 +5036,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8,<4.0"
-content-hash = "d0c953a0bc891c3e9a7988486072649d6303c9d6aa3e02f8019928a9b2e7f04e"
+content-hash = "53764671f607ff4a297cf85ccbcffd10d125b571c8b9bdf6e00e79a92c2991ce"
diff --git a/pyproject.toml b/pyproject.toml
index 270ab5859..4dbcd53f0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -66,7 +66,7 @@ nbval = "^0.10.0"
 
 [tool.poetry.group.jupyter.dependencies]
 jupyterlab = ">=3.5.0"
-matplotlib = "3.7"
+ipywidgets = "^8.1.1"
 
 [tool.poetry.group.docs.dependencies]
 sphinx = {version = "5.2.3" }

From fb68ba8f0e7e9b14ac344bc4efdfe5d61adda671 Mon Sep 17 00:00:00 2001
From: Bryce Meyer <bryce13950@gmail.com>
Date: Thu, 26 Oct 2023 23:20:29 +0200
Subject: [PATCH 22/25] recalculated hash

---
 poetry.lock | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 66d125137..fa502a3ca 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -2705,8 +2705,8 @@ files = [
 [package.dependencies]
 numpy = [
     {version = ">=1.20.3", markers = "python_version < \"3.10\""},
-    {version = ">=1.21.0", markers = "python_version >= \"3.10\" and python_version < \"3.11\""},
     {version = ">=1.23.2", markers = "python_version >= \"3.11\""},
+    {version = ">=1.21.0", markers = "python_version >= \"3.10\" and python_version < \"3.11\""},
 ]
 python-dateutil = ">=2.8.2"
 pytz = ">=2020.1"
@@ -5132,4 +5132,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8,<4.0"
-content-hash = "1e0494a4d19fa81fb71511eb61381319af9298bb90e02c5d3307507e09235fe6"
+content-hash = "e8f11aaeb233db6e483ea1e2e3d873ca5e84330f7b3bb6a1065a0a14f05fc60e"

From 9393e01d8017f5d83457038f4089ba5a40804c00 Mon Sep 17 00:00:00 2001
From: Bryce Meyer <bryce13950@gmail.com>
Date: Thu, 26 Oct 2023 23:23:17 +0200
Subject: [PATCH 23/25] removed inline matplotlib reference

---
 demos/Main_Demo.ipynb | 1 -
 1 file changed, 1 deletion(-)

diff --git a/demos/Main_Demo.ipynb b/demos/Main_Demo.ipynb
index f17e7dec7..a49d75764 100644
--- a/demos/Main_Demo.ipynb
+++ b/demos/Main_Demo.ipynb
@@ -707,7 +707,6 @@
                 }
             ],
             "source": [
-                "%matplotlib inline\n",
                 "# Add the index to the end of the label, because plotly doesn't like duplicate labels\n",
                 "token_labels = [f\"{token}_{index}\" for index, token in enumerate(model.to_str_tokens(clean_tokens))]\n",
                 "imshow(ioi_patching_result, x=token_labels, xaxis=\"Position\", yaxis=\"Layer\", title=\"Normalized Logit Difference After Patching Residual Stream on the IOI Task\")"

From d53df90548d2301333fac84c61e40b5b151fd68f Mon Sep 17 00:00:00 2001
From: Bryce Meyer <bryce13950@gmail.com>
Date: Thu, 26 Oct 2023 23:37:09 +0200
Subject: [PATCH 24/25] regenerated output and ignored cell

---
 demos/Main_Demo.ipynb | 220 ++++++++++++++++++++----------------------
 1 file changed, 107 insertions(+), 113 deletions(-)

diff --git a/demos/Main_Demo.ipynb b/demos/Main_Demo.ipynb
index a49d75764..1ee01430d 100644
--- a/demos/Main_Demo.ipynb
+++ b/demos/Main_Demo.ipynb
@@ -45,7 +45,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 158,
+            "execution_count": 292,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -75,13 +75,12 @@
                 "        %autoreload 2\n",
                 "        \n",
                 "IN_GITHUB = os.getenv(\"GITHUB_ACTIONS\") == \"true\"\n",
-                "IN_GITHUB = True\n",
-                "    "
+                "IN_GITHUB = True\n"
             ]
         },
         {
             "cell_type": "code",
-            "execution_count": 159,
+            "execution_count": 293,
             "metadata": {},
             "outputs": [
                 {
@@ -104,27 +103,27 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 160,
+            "execution_count": 294,
             "metadata": {},
             "outputs": [
                 {
                     "data": {
                         "text/html": [
-                            "<div id=\"circuits-vis-1e971456-b1dc\" style=\"margin: 15px 0;\"/>\n",
+                            "<div id=\"circuits-vis-7e4c8a75-1335\" style=\"margin: 15px 0;\"/>\n",
                             "    <script crossorigin type=\"module\">\n",
                             "    import { render, Hello } from \"https://unpkg.com/circuitsvis@1.43.0/dist/cdn/esm.js\";\n",
                             "    render(\n",
-                            "      \"circuits-vis-1e971456-b1dc\",\n",
+                            "      \"circuits-vis-7e4c8a75-1335\",\n",
                             "      Hello,\n",
                             "      {\"name\": \"Neel\"}\n",
                             "    )\n",
                             "    </script>"
                         ],
                         "text/plain": [
-                            "<circuitsvis.utils.render.RenderedHTML at 0xffff1014d280>"
+                            "<circuitsvis.utils.render.RenderedHTML at 0xffff10cc9f10>"
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                         "text/html": {
                             "Content-Type": "text/html"
@@ -141,7 +140,7 @@
         },
         {
             "cell_type": "code",
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+            "execution_count": 295,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -159,7 +158,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 162,
+            "execution_count": 296,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -180,16 +179,16 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 163,
+            "execution_count": 297,
             "metadata": {},
             "outputs": [
                 {
                     "data": {
                         "text/plain": [
-                            "<torch.autograd.grad_mode.set_grad_enabled at 0xffff037d2460>"
+                            "<torch.autograd.grad_mode.set_grad_enabled at 0xffff425948e0>"
                         ]
                     },
-                    "execution_count": 163,
+                    "execution_count": 297,
                     "metadata": {},
                     "output_type": "execute_result"
                 }
@@ -207,7 +206,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 164,
+            "execution_count": 298,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -255,7 +254,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 165,
+            "execution_count": 299,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -264,7 +263,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 166,
+            "execution_count": 300,
             "metadata": {},
             "outputs": [
                 {
@@ -301,7 +300,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 167,
+            "execution_count": 301,
             "metadata": {},
             "outputs": [
                 {
@@ -339,7 +338,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 168,
+            "execution_count": 302,
             "metadata": {},
             "outputs": [
                 {
@@ -373,7 +372,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 169,
+            "execution_count": 303,
             "metadata": {},
             "outputs": [
                 {
@@ -394,7 +393,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 170,
+            "execution_count": 304,
             "metadata": {},
             "outputs": [
                 {
@@ -407,21 +406,21 @@
                 {
                     "data": {
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+                            "<div id=\"circuits-vis-44fcf4fd-e695\" style=\"margin: 15px 0;\"/>\n",
                             "    <script crossorigin type=\"module\">\n",
                             "    import { render, AttentionPatterns } from \"https://unpkg.com/circuitsvis@1.43.0/dist/cdn/esm.js\";\n",
                             "    render(\n",
-                            "      \"circuits-vis-c465a148-407d\",\n",
+                            "      \"circuits-vis-44fcf4fd-e695\",\n",
                             "      AttentionPatterns,\n",
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                             "                            \n",
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@@ -908,24 +907,24 @@
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+            "execution_count": 311,
             "metadata": {},
             "outputs": [
                 {
                     "data": {
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                         "text/plain": [
-                            "<circuitsvis.utils.render.RenderedHTML at 0xffff10fadf40>"
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@@ -995,7 +994,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 178,
+            "execution_count": 312,
             "metadata": {},
             "outputs": [
                 {
@@ -1020,7 +1019,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 179,
+            "execution_count": 313,
             "metadata": {},
             "outputs": [
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@@ -1030,9 +1029,9 @@
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                             "                            \n",
-                            "var gd = document.getElementById('bec758c8-b743-473a-bebd-04e732cee347');\n",
+                            "var gd = document.getElementById('378fa384-2cc7-4a1c-b871-c643c0e527e8');\n",
                             "var x = new MutationObserver(function (mutations, observer) {{\n",
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@@ -1211,7 +1210,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 180,
+            "execution_count": 314,
             "metadata": {},
             "outputs": [
                 {
@@ -1248,7 +1247,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 181,
+            "execution_count": 315,
             "metadata": {},
             "outputs": [
                 {
@@ -1286,7 +1285,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 182,
+            "execution_count": 316,
             "metadata": {},
             "outputs": [
                 {
@@ -1305,13 +1304,7 @@
                         "blocks.0.ln1.hook_normalized torch.Size([1, 10, 768])\n",
                         "blocks.0.attn.hook_q torch.Size([1, 10, 12, 64])\n",
                         "blocks.0.attn.hook_k torch.Size([1, 10, 12, 64])\n",
-                        "blocks.0.attn.hook_v torch.Size([1, 10, 12, 64])\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
+                        "blocks.0.attn.hook_v torch.Size([1, 10, 12, 64])\n",
                         "blocks.0.attn.hook_attn_scores torch.Size([1, 12, 10, 10])\n",
                         "blocks.0.attn.hook_pattern torch.Size([1, 12, 10, 10])\n",
                         "blocks.0.attn.hook_z torch.Size([1, 10, 12, 64])\n",
@@ -1382,7 +1375,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 183,
+            "execution_count": 317,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -1392,7 +1385,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 184,
+            "execution_count": 318,
             "metadata": {},
             "outputs": [
                 {
@@ -1466,7 +1459,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 185,
+            "execution_count": 319,
             "metadata": {},
             "outputs": [
                 {
@@ -1527,7 +1520,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 186,
+            "execution_count": 320,
             "metadata": {},
             "outputs": [
                 {
@@ -1553,7 +1546,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 187,
+            "execution_count": 321,
             "metadata": {},
             "outputs": [
                 {
@@ -1586,7 +1579,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 188,
+            "execution_count": 322,
             "metadata": {},
             "outputs": [
                 {
@@ -1622,7 +1615,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 189,
+            "execution_count": 323,
             "metadata": {},
             "outputs": [
                 {
@@ -1655,7 +1648,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 190,
+            "execution_count": 324,
             "metadata": {},
             "outputs": [
                 {
@@ -1686,7 +1679,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 191,
+            "execution_count": 325,
             "metadata": {},
             "outputs": [
                 {
@@ -1712,7 +1705,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 192,
+            "execution_count": 326,
             "metadata": {},
             "outputs": [
                 {
@@ -1744,7 +1737,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 193,
+            "execution_count": 327,
             "metadata": {},
             "outputs": [
                 {
@@ -1788,7 +1781,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 194,
+            "execution_count": 328,
             "metadata": {},
             "outputs": [
                 {
@@ -1822,7 +1815,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 195,
+            "execution_count": 329,
             "metadata": {},
             "outputs": [
                 {
@@ -1855,7 +1848,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 196,
+            "execution_count": 330,
             "metadata": {},
             "outputs": [
                 {
@@ -1908,7 +1901,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 197,
+            "execution_count": 331,
             "metadata": {},
             "outputs": [
                 {
@@ -1946,7 +1939,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 198,
+            "execution_count": 332,
             "metadata": {},
             "outputs": [
                 {
@@ -1965,6 +1958,7 @@
                 }
             ],
             "source": [
+                "# NBVAL_IGNORE_OUTPUT\n",
                 "print(\"Eigenvalues:\")\n",
                 "print(torch.linalg.eig(AB).eigenvalues)\n",
                 "print(AB_factor.eigenvalues)\n",
@@ -1983,7 +1977,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 199,
+            "execution_count": 333,
             "metadata": {},
             "outputs": [
                 {
@@ -2018,7 +2012,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 200,
+            "execution_count": 334,
             "metadata": {},
             "outputs": [
                 {
@@ -2061,7 +2055,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 201,
+            "execution_count": 335,
             "metadata": {},
             "outputs": [
                 {
@@ -2079,7 +2073,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 202,
+            "execution_count": 336,
             "metadata": {},
             "outputs": [
                 {
@@ -2099,7 +2093,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 203,
+            "execution_count": 337,
             "metadata": {},
             "outputs": [
                 {
@@ -2109,9 +2103,9 @@
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                             "                            \n",
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+            "execution_count": 338,
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                             "                            \n",
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@@ -2356,7 +2350,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 209,
+            "execution_count": 343,
             "metadata": {},
             "outputs": [
                 {
@@ -2393,13 +2387,13 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 210,
+            "execution_count": 344,
             "metadata": {},
             "outputs": [
                 {
                     "data": {
                         "application/vnd.jupyter.widget-view+json": {
-                            "model_id": "7d4cf44ad1c1496f833dd2ee1e32db11",
+                            "model_id": "f16e699caef243e3bd730cd876600c4a",
                             "version_major": 2,
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@@ -2416,7 +2410,7 @@
                             "'(CNN) President Barack Obama caught in embarrassing new scandal\\n\\nAmerican voters who backed Hillary Clinton gave President Barack Obama a 9.5-point lead over Republican Mitt Romney in the latest CNN/ORC International poll, his lowest level since the last CNN-ORC poll in 2006.\\n\\nRepublican voters'"
                         ]
                     },
-                    "execution_count": 210,
+                    "execution_count": 344,
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@@ -2472,7 +2466,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 211,
+            "execution_count": 345,
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@@ -2530,7 +2524,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 212,
+            "execution_count": 346,
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             "outputs": [
                 {
@@ -2565,7 +2559,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 213,
+            "execution_count": 347,
             "metadata": {},
             "outputs": [
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@@ -2632,7 +2626,7 @@
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         {
             "cell_type": "code",
-            "execution_count": 214,
+            "execution_count": 348,
             "metadata": {},
             "outputs": [
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                             "                            \n",
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@@ -2872,7 +2866,7 @@
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@@ -2895,7 +2889,7 @@
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@@ -2923,7 +2917,7 @@
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From 3425b8db4b01b888aa1ff3a36d8d23627f146b5b Mon Sep 17 00:00:00 2001
From: Bryce Meyer <bryce13950@gmail.com>
Date: Fri, 27 Oct 2023 23:23:53 +0200
Subject: [PATCH 25/25] fixed url

---
 demos/Main_Demo.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/demos/Main_Demo.ipynb b/demos/Main_Demo.ipynb
index 1ee01430d..d5d524c76 100644
--- a/demos/Main_Demo.ipynb
+++ b/demos/Main_Demo.ipynb
@@ -235,7 +235,7 @@
             "cell_type": "markdown",
             "metadata": {},
             "source": [
-                "This is a demo notebook for [TransformerLens](https://github.com/neelnanda-io/TransformerLens), **a library I ([Neel Nanda](neelnanda.io)) wrote for doing [mechanistic interpretability](https://distill.pub/2020/circuits/zoom-in/) of GPT-2 Style language models.** The goal of mechanistic interpretability is to take a trained model and reverse engineer the algorithms the model learned during training from its weights. It is a fact about the world today that we have computer programs that can essentially speak English at a human level (GPT-3, PaLM, etc), yet we have no idea how they work nor how to write one ourselves. This offends me greatly, and I would like to solve this! Mechanistic interpretability is a very young and small field, and there are a *lot* of open problems - if you would like to help, please try working on one! **If you want to skill up, check out [my guide to getting started](https://neelnanda.io/getting-started), and if you want to jump into an open problem check out my sequence [200 Concrete Open Problems in Mechanistic Interpretability](https://neelnanda.io/concrete-open-problems).**\n",
+                "This is a demo notebook for [TransformerLens](https://github.com/neelnanda-io/TransformerLens), **a library I ([Neel Nanda](https://neelnanda.io)) wrote for doing [mechanistic interpretability](https://distill.pub/2020/circuits/zoom-in/) of GPT-2 Style language models.** The goal of mechanistic interpretability is to take a trained model and reverse engineer the algorithms the model learned during training from its weights. It is a fact about the world today that we have computer programs that can essentially speak English at a human level (GPT-3, PaLM, etc), yet we have no idea how they work nor how to write one ourselves. This offends me greatly, and I would like to solve this! Mechanistic interpretability is a very young and small field, and there are a *lot* of open problems - if you would like to help, please try working on one! **If you want to skill up, check out [my guide to getting started](https://neelnanda.io/getting-started), and if you want to jump into an open problem check out my sequence [200 Concrete Open Problems in Mechanistic Interpretability](https://neelnanda.io/concrete-open-problems).**\n",
                 "\n",
                 "I wrote this library because after I left the Anthropic interpretability team and started doing independent research, I got extremely frustrated by the state of open source tooling. There's a lot of excellent infrastructure like HuggingFace and DeepSpeed to *use* or *train* models, but very little to dig into their internals and reverse engineer how they work. **This library tries to solve that**, and to make it easy to get into the field even if you don't work at an industry org with real infrastructure! The core features were heavily inspired by [Anthropic's excellent Garcon tool](https://transformer-circuits.pub/2021/garcon/index.html). Credit to Nelson Elhage and Chris Olah for building Garcon and showing me the value of good infrastructure for accelerating exploratory research!\n",
                 "\n",