{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cdC77TomGBq-",
    "outputId": "53895fc2-17ab-412d-9eeb-681e594aac7a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "zsh:1: command not found: nvidia-smi\r\n"
     ]
    }
   ],
   "source": [
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YSW4FUQPwIYu",
    "outputId": "853551ae-5155-46d1-f61c-16e2f2627237"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloning into 'alpaca-lora'...\n",
      "remote: Enumerating objects: 607, done.\u001b[K\n",
      "remote: Counting objects: 100% (51/51), done.\u001b[K\n",
      "remote: Compressing objects: 100% (32/32), done.\u001b[K\n",
      "remote: Total 607 (delta 28), reused 34 (delta 19), pack-reused 556\u001b[K\n",
      "Receiving objects: 100% (607/607), 27.78 MiB | 4.33 MiB/s, done.\n",
      "Resolving deltas: 100% (360/360), done.\n"
     ]
    }
   ],
   "source": [
    "!git clone https://github.com/tloen/alpaca-lora.git"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Gzg8SopX8EWH"
   },
   "source": [
    "# Alpaca LLaMa 7B LoRa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "s1xm2uERx_st",
    "outputId": "e854ebc8-88fc-4964-b586-9243869f16ec"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/dano/DEV/smartscraper/smartscraper/alpaca-lora\n"
     ]
    }
   ],
   "source": [
    "%cd alpaca-lora/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JCB9UzMVwsSM",
    "outputId": "78c8e31d-d978-44fa-c250-b862e83eb3c1"
   },
   "outputs": [],
   "source": [
    "!pip install -q datasets loralib sentencepiece\n",
    "\n",
    "!pip uninstall -y transformers\n",
    "!pip install -q git+https://github.com/zphang/transformers@c3dc391\n",
    "# pip install git+https://github.com/zphang/transformers.git@llama_push\n",
    "\n",
    "!pip install -q git+https://github.com/huggingface/peft.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qCnXTszZxE2T",
    "outputId": "1c54a473-fe21-4094-ff32-90ca84df62d8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
      "Collecting bitsandbytes\n",
      "  Downloading bitsandbytes-0.38.1-py3-none-any.whl (104.3 MB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m104.3/104.3 MB\u001b[0m \u001b[31m9.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: bitsandbytes\n",
      "Successfully installed bitsandbytes-0.38.1\n"
     ]
    }
   ],
   "source": [
    "!pip install bitsandbytes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9w0aSCzhxxQf"
   },
   "source": [
    "## Data Check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265,
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      "c9d74aaecc494192a4183283fa1c5c53",
      "626bfaa8bdba4a22aac33c44bdfa23b8",
      "b940bdfc19ca4c11b8069db149c3d4d7",
      "3f4ffb32acec47d098e400626aa5d302",
      "026cf8ce96ef416e9ad01926c16a7283",
      "2b963f2077bf465bb390370c146757a8"
     ]
    },
    "id": "OdgRTo5YxyRL",
    "outputId": "d199eaf2-1343-4240-bb77-7d16780904aa"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "81876a99e1254a2da9e13b27119dee33",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b494c95054564e66b9d8ee306b7caf71",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/2.00 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5973761291654eee88d7ac3daed7f9f0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)okenizer_config.json:   0%|          | 0.00/141 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset json/default to /root/.cache/huggingface/datasets/json/default-8b6abf24a2251c59/0.0.0/fe5dd6ea2639a6df622901539cb550cf8797e5a6b2dd7af1cf934bed8e233e6e...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dddf063c9158417591e17e27ac85b371",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c0c1220b827940a599ba3e3da308aed1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fda987f322f045a39c7339d11dea8789",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset json downloaded and prepared to /root/.cache/huggingface/datasets/json/default-8b6abf24a2251c59/0.0.0/fe5dd6ea2639a6df622901539cb550cf8797e5a6b2dd7af1cf934bed8e233e6e. Subsequent calls will reuse this data.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "29d21532a80e4d8fac4f297fa33709d6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a09b73ac94a94022a5792a224b1ec4d0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/52002 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "from transformers import LLaMATokenizer\n",
    "\n",
    "\n",
    "tokenizer = LLaMATokenizer.from_pretrained(\"decapoda-research/llama-7b-hf\", add_eos_token=True)\n",
    "tokenizer.pad_token = tokenizer.eos_token\n",
    "tokenizer.pad_token_id = tokenizer.eos_token_id\n",
    "\n",
    "data = load_dataset(\"json\", data_files=\"alpaca_data.json\")\n",
    "\n",
    "\n",
    "def generate_prompt(data_point):\n",
    "    # sorry about the formatting disaster gotta move fast\n",
    "    if data_point[\"input\"]:\n",
    "        return f\"\"\"\n",
    "### Instruction:\n",
    "{data_point[\"instruction\"]}\n",
    "\n",
    "### Input:\n",
    "{data_point[\"input\"]}\n",
    "\n",
    "### Response:\n",
    "{data_point[\"output\"]}\"\"\"\n",
    "    else:\n",
    "        return f\"\"\"\n",
    "### Instruction:\n",
    "{data_point[\"instruction\"]}\n",
    "\n",
    "### Response:\n",
    "{data_point[\"output\"]}\"\"\"\n",
    "\n",
    "\n",
    "data = data.map(lambda data_point: {\"prompt\": tokenizer(generate_prompt(data_point))})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "j7qZqe0YxG2c"
   },
   "source": [
    "## Fine Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 74
    },
    "id": "PBjzCrSF82IS",
    "outputId": "e5766286-6afa-418d-89b5-25f3f820bcc2"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "     <input type=\"file\" id=\"files-5285696c-4cbd-41fc-baad-b3b122481f86\" name=\"files[]\" multiple disabled\n",
       "        style=\"border:none\" />\n",
       "     <output id=\"result-5285696c-4cbd-41fc-baad-b3b122481f86\">\n",
       "      Upload widget is only available when the cell has been executed in the\n",
       "      current browser session. Please rerun this cell to enable.\n",
       "      </output>\n",
       "      <script>// Copyright 2017 Google LLC\n",
       "//\n",
       "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
       "// you may not use this file except in compliance with the License.\n",
       "// You may obtain a copy of the License at\n",
       "//\n",
       "//      http://www.apache.org/licenses/LICENSE-2.0\n",
       "//\n",
       "// Unless required by applicable law or agreed to in writing, software\n",
       "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
       "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
       "// See the License for the specific language governing permissions and\n",
       "// limitations under the License.\n",
       "\n",
       "/**\n",
       " * @fileoverview Helpers for google.colab Python module.\n",
       " */\n",
       "(function(scope) {\n",
       "function span(text, styleAttributes = {}) {\n",
       "  const element = document.createElement('span');\n",
       "  element.textContent = text;\n",
       "  for (const key of Object.keys(styleAttributes)) {\n",
       "    element.style[key] = styleAttributes[key];\n",
       "  }\n",
       "  return element;\n",
       "}\n",
       "\n",
       "// Max number of bytes which will be uploaded at a time.\n",
       "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
       "\n",
       "function _uploadFiles(inputId, outputId) {\n",
       "  const steps = uploadFilesStep(inputId, outputId);\n",
       "  const outputElement = document.getElementById(outputId);\n",
       "  // Cache steps on the outputElement to make it available for the next call\n",
       "  // to uploadFilesContinue from Python.\n",
       "  outputElement.steps = steps;\n",
       "\n",
       "  return _uploadFilesContinue(outputId);\n",
       "}\n",
       "\n",
       "// This is roughly an async generator (not supported in the browser yet),\n",
       "// where there are multiple asynchronous steps and the Python side is going\n",
       "// to poll for completion of each step.\n",
       "// This uses a Promise to block the python side on completion of each step,\n",
       "// then passes the result of the previous step as the input to the next step.\n",
       "function _uploadFilesContinue(outputId) {\n",
       "  const outputElement = document.getElementById(outputId);\n",
       "  const steps = outputElement.steps;\n",
       "\n",
       "  const next = steps.next(outputElement.lastPromiseValue);\n",
       "  return Promise.resolve(next.value.promise).then((value) => {\n",
       "    // Cache the last promise value to make it available to the next\n",
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       "    outputElement.lastPromiseValue = value;\n",
       "    return next.value.response;\n",
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       "}\n",
       "\n",
       "/**\n",
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       " * process.\n",
       " * @param {string} inputId Element ID of the input file picker element.\n",
       " * @param {string} outputId Element ID of the output display.\n",
       " * @return {!Iterable<!Object>} Iterable of next steps.\n",
       " */\n",
       "function* uploadFilesStep(inputId, outputId) {\n",
       "  const inputElement = document.getElementById(inputId);\n",
       "  inputElement.disabled = false;\n",
       "\n",
       "  const outputElement = document.getElementById(outputId);\n",
       "  outputElement.innerHTML = '';\n",
       "\n",
       "  const pickedPromise = new Promise((resolve) => {\n",
       "    inputElement.addEventListener('change', (e) => {\n",
       "      resolve(e.target.files);\n",
       "    });\n",
       "  });\n",
       "\n",
       "  const cancel = document.createElement('button');\n",
       "  inputElement.parentElement.appendChild(cancel);\n",
       "  cancel.textContent = 'Cancel upload';\n",
       "  const cancelPromise = new Promise((resolve) => {\n",
       "    cancel.onclick = () => {\n",
       "      resolve(null);\n",
       "    };\n",
       "  });\n",
       "\n",
       "  // Wait for the user to pick the files.\n",
       "  const files = yield {\n",
       "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
       "    response: {\n",
       "      action: 'starting',\n",
       "    }\n",
       "  };\n",
       "\n",
       "  cancel.remove();\n",
       "\n",
       "  // Disable the input element since further picks are not allowed.\n",
       "  inputElement.disabled = true;\n",
       "\n",
       "  if (!files) {\n",
       "    return {\n",
       "      response: {\n",
       "        action: 'complete',\n",
       "      }\n",
       "    };\n",
       "  }\n",
       "\n",
       "  for (const file of files) {\n",
       "    const li = document.createElement('li');\n",
       "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
       "    li.append(span(\n",
       "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
       "        `last modified: ${\n",
       "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
       "                                    'n/a'} - `));\n",
       "    const percent = span('0% done');\n",
       "    li.appendChild(percent);\n",
       "\n",
       "    outputElement.appendChild(li);\n",
       "\n",
       "    const fileDataPromise = new Promise((resolve) => {\n",
       "      const reader = new FileReader();\n",
       "      reader.onload = (e) => {\n",
       "        resolve(e.target.result);\n",
       "      };\n",
       "      reader.readAsArrayBuffer(file);\n",
       "    });\n",
       "    // Wait for the data to be ready.\n",
       "    let fileData = yield {\n",
       "      promise: fileDataPromise,\n",
       "      response: {\n",
       "        action: 'continue',\n",
       "      }\n",
       "    };\n",
       "\n",
       "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
       "    let position = 0;\n",
       "    do {\n",
       "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
       "      const chunk = new Uint8Array(fileData, position, length);\n",
       "      position += length;\n",
       "\n",
       "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
       "      yield {\n",
       "        response: {\n",
       "          action: 'append',\n",
       "          file: file.name,\n",
       "          data: base64,\n",
       "        },\n",
       "      };\n",
       "\n",
       "      let percentDone = fileData.byteLength === 0 ?\n",
       "          100 :\n",
       "          Math.round((position / fileData.byteLength) * 100);\n",
       "      percent.textContent = `${percentDone}% done`;\n",
       "\n",
       "    } while (position < fileData.byteLength);\n",
       "  }\n",
       "\n",
       "  // All done.\n",
       "  yield {\n",
       "    response: {\n",
       "      action: 'complete',\n",
       "    }\n",
       "  };\n",
       "}\n",
       "\n",
       "scope.google = scope.google || {};\n",
       "scope.google.colab = scope.google.colab || {};\n",
       "scope.google.colab._files = {\n",
       "  _uploadFiles,\n",
       "  _uploadFilesContinue,\n",
       "};\n",
       "})(self);\n",
       "</script> "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving samples.json to samples.json\n"
     ]
    }
   ],
   "source": [
    "from google.colab import files\n",
    "uploaded = files.upload()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!conda install -y -c conda-forge cudatoolkit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import os\n",
    "\n",
    "# # Set PATH variable\n",
    "# cuda_path = \"/usr/local/cuda/bin\"  # Update this to the correct path on your system\n",
    "# os.environ[\"PATH\"] = f\"{cuda_path}:{os.environ['PATH']}\"\n",
    "\n",
    "# # Set LD_LIBRARY_PATH variable\n",
    "# cuda_lib_path = \"/usr/local/cuda/lib64\"  # Update this to the correct path on your system\n",
    "# os.environ[\"LD_LIBRARY_PATH\"] = f\"{cuda_lib_path}:{os.environ.get('LD_LIBRARY_PATH', '')}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !cp /opt/conda/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cuda117.so /opt/conda/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cpu.so"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "EQyg3MygxKCW",
    "outputId": "627fc966-3f96-4d2e-8761-d508afe7ea40",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "===================================BUG REPORT===================================\n",
      "Welcome to bitsandbytes. For bug reports, please run\n",
      "\n",
      "python -m bitsandbytes\n",
      "\n",
      " and submit this information together with your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
      "================================================================================\n",
      "bin /usr/local/lib/python3.9/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so\n",
      "CUDA_SETUP: WARNING! libcudart.so not found in any environmental path. Searching in backup paths...\n",
      "CUDA SETUP: CUDA runtime path found: /usr/local/cuda/lib64/libcudart.so\n",
      "CUDA SETUP: Highest compute capability among GPUs detected: 7.5\n",
      "CUDA SETUP: Detected CUDA version 118\n",
      "CUDA SETUP: Loading binary /usr/local/lib/python3.9/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: /usr/lib64-nvidia did not contain ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] as expected! Searching further paths...\n",
      "  warn(msg)\n",
      "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/sys/fs/cgroup/memory.events /var/colab/cgroup/jupyter-children/memory.events')}\n",
      "  warn(msg)\n",
      "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('http'), PosixPath('8013'), PosixPath('//172.28.0.1')}\n",
      "  warn(msg)\n",
      "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('//colab.research.google.com/tun/m/cc48301118ce562b961b3c22d803539adc1e0c19/gpu-t4-hm-2dzai8ohfny1z --tunnel_background_save_delay=10s --tunnel_periodic_background_save_frequency=30m0s --enable_output_coalescing=true --output_coalescing_required=true'), PosixPath('--listen_host=172.28.0.12 --target_host=172.28.0.12 --tunnel_background_save_url=https')}\n",
      "  warn(msg)\n",
      "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/env/python')}\n",
      "  warn(msg)\n",
      "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('module'), PosixPath('//ipykernel.pylab.backend_inline')}\n",
      "  warn(msg)\n",
      "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: Found duplicate ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] files: {PosixPath('/usr/local/cuda/lib64/libcudart.so'), PosixPath('/usr/local/cuda/lib64/libcudart.so.11.0')}.. We'll flip a coin and try one of these, in order to fail forward.\n",
      "Either way, this might cause trouble in the future:\n",
      "If you get `CUDA error: invalid device function` errors, the above might be the cause and the solution is to make sure only one ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] in the paths that we search based on your env.\n",
      "  warn(msg)\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "# os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import bitsandbytes as bnb\n",
    "from datasets import load_dataset\n",
    "import transformers\n",
    "from transformers import AutoTokenizer, AutoConfig, LLaMAForCausalLM, LLaMATokenizer\n",
    "from peft import prepare_model_for_int8_training, LoraConfig, get_peft_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "76iZTtGJy26x"
   },
   "outputs": [],
   "source": [
    "# Setting for A100 - For 3090 \n",
    "MICRO_BATCH_SIZE = 8  # change to 4 for 3090\n",
    "BATCH_SIZE = 128\n",
    "GRADIENT_ACCUMULATION_STEPS = BATCH_SIZE // MICRO_BATCH_SIZE\n",
    "EPOCHS = 2  # paper uses 3\n",
    "LEARNING_RATE = 2e-5  # from the original paper\n",
    "LORA_R = 4\n",
    "LORA_ALPHA = 16\n",
    "LORA_DROPOUT = 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Overriding torch_dtype=None with `torch_dtype=torch.float16` due to requirements of `bitsandbytes` to enable model loading in mixed int8. Either pass torch_dtype=torch.float16 or don't pass this argument at all to remove this warning.\n"
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      ]
     },
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    },
    {
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      ]
     },
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    }
   ],
   "source": [
    "model = LLaMAForCausalLM.from_pretrained(\n",
    "    \"decapoda-research/llama-7b-hf\",\n",
    "    load_in_8bit=True,\n",
    "    device_map=\"auto\",\n",
    ")\n",
    "tokenizer = LLaMATokenizer.from_pretrained(\n",
    "    \"decapoda-research/llama-7b-hf\", add_eos_token=True\n",
    ")\n",
    "\n",
    "model = prepare_model_for_int8_training(model)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "Oh-jJFKNzBpw"
   },
   "outputs": [],
   "source": [
    "config = LoraConfig(\n",
    "    r=LORA_R,\n",
    "    lora_alpha=LORA_ALPHA,\n",
    "    target_modules=[\"q_proj\", \"v_proj\"],\n",
    "    lora_dropout=LORA_DROPOUT,\n",
    "    bias=\"none\",\n",
    "    task_type=\"CAUSAL_LM\",\n",
    ")\n",
    "model = get_peft_model(model, config)\n",
    "tokenizer.pad_token_id = 0  # unk. we want this to be different from the eos token\n",
    "data = load_dataset(\"json\", data_files=\"samples.json\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KiQk4WtJDUUy",
    "outputId": "3ac05596-5221-4f8f-fc91-7b2a6e04b35f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/bin/bash: nvidia-smi: command not found\n"
     ]
    }
   ],
   "source": [
    "torch.cuda.empty_cache()\n",
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
      "3ab67f8508924be185551bb31fb81796",
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      "298902b16b784c9989af0b6e9df35ed5",
      "f1fa0da70f8847bb85c2a1971e5bdac9",
      "1f28738e0a5842c1bcda9012b19a7d37",
      "280a95084fdd46d0a0a5a017c6a90dfd",
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      "948428bb4fe6474f92f72e3a76ac435f",
      "84c7481e57d04c3c88117abb4000e8cf",
      "2ef5879698e94bb299b1f2c257c8f5c8"
     ]
    },
    "id": "kWP89TPIwRkK",
    "outputId": "5160b20f-d883-4252-c8e2-82d018248e15"
   },
   "outputs": [
    {
     "data": {
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       "model_id": "3ab67f8508924be185551bb31fb81796",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='190' max='1250' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [ 190/1250 34:10 < 3:12:43, 0.09 it/s, Epoch 0.30/2]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.589500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.522800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.538200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.563900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.385800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.604400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.510900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.636800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1.648500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1.514400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>1.735600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>1.547700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>1.551700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1.626300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1.601700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>1.546900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1.418700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>1.601500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>1.590600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1.529000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>1.496400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>1.522000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>1.510200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>1.451700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>1.396100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>1.563300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>1.566200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>1.508400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>1.709600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>1.526800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>1.508000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>1.558800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>1.419200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>1.692300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>1.399700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>1.520800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>1.574000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>1.473200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>1.586500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>1.525700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>1.539600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>1.503100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>1.678000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>1.667800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>1.611900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>1.451400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>1.578500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>1.505500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>1.464900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>1.507800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>1.604900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>1.549800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>1.530200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>1.472800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>1.640500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>1.436500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>1.517300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>1.659700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>1.643600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>1.649800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>1.522300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>1.499000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>1.466100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>1.590900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>1.569200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>1.550200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>1.529300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>1.485400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>1.507400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>1.630400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>1.573100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>1.466800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>1.520100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>1.532600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>1.538000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>1.548100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>1.620600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>1.511000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>1.522400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>1.528100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>1.571100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>1.578600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>1.640200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>1.472900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>85</td>\n",
       "      <td>1.528800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>86</td>\n",
       "      <td>1.565300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>1.548700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88</td>\n",
       "      <td>1.535500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>1.580800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>1.570300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>1.753400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>92</td>\n",
       "      <td>1.631900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>1.368000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>1.694900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>1.581200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>1.628700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>1.612800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>1.597300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>1.572200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>100</td>\n",
       "      <td>1.638800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>101</td>\n",
       "      <td>1.500800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>102</td>\n",
       "      <td>1.389500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>103</td>\n",
       "      <td>1.516100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>104</td>\n",
       "      <td>1.523800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>105</td>\n",
       "      <td>1.534100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>106</td>\n",
       "      <td>1.488400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>107</td>\n",
       "      <td>1.540200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>108</td>\n",
       "      <td>1.418500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>109</td>\n",
       "      <td>1.417900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>110</td>\n",
       "      <td>1.482700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>111</td>\n",
       "      <td>1.562400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>112</td>\n",
       "      <td>1.519900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>113</td>\n",
       "      <td>1.461000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>114</td>\n",
       "      <td>1.451600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>115</td>\n",
       "      <td>1.571600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>116</td>\n",
       "      <td>1.547100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>117</td>\n",
       "      <td>1.626800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>118</td>\n",
       "      <td>1.342200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>119</td>\n",
       "      <td>1.505400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>120</td>\n",
       "      <td>1.613100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>121</td>\n",
       "      <td>1.564500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>122</td>\n",
       "      <td>1.439300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>123</td>\n",
       "      <td>1.557200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>124</td>\n",
       "      <td>1.554900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>125</td>\n",
       "      <td>1.616000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>126</td>\n",
       "      <td>1.610700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>127</td>\n",
       "      <td>1.491200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>128</td>\n",
       "      <td>1.610400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>129</td>\n",
       "      <td>1.578500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>130</td>\n",
       "      <td>1.554900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>131</td>\n",
       "      <td>1.578000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>132</td>\n",
       "      <td>1.667500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>133</td>\n",
       "      <td>1.488000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>134</td>\n",
       "      <td>1.656300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>135</td>\n",
       "      <td>1.569100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>136</td>\n",
       "      <td>1.563600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>137</td>\n",
       "      <td>1.542000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>138</td>\n",
       "      <td>1.558300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>139</td>\n",
       "      <td>1.588600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>140</td>\n",
       "      <td>1.600200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>141</td>\n",
       "      <td>1.626200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>142</td>\n",
       "      <td>1.478100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>143</td>\n",
       "      <td>1.550800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>144</td>\n",
       "      <td>1.644200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>145</td>\n",
       "      <td>1.513400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>146</td>\n",
       "      <td>1.517500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>147</td>\n",
       "      <td>1.439200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>148</td>\n",
       "      <td>1.444200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>149</td>\n",
       "      <td>1.552200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>150</td>\n",
       "      <td>1.504400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>151</td>\n",
       "      <td>1.472100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>152</td>\n",
       "      <td>1.511900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>153</td>\n",
       "      <td>1.563300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>154</td>\n",
       "      <td>1.387600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>155</td>\n",
       "      <td>1.483200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>156</td>\n",
       "      <td>1.637500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>157</td>\n",
       "      <td>1.664000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>158</td>\n",
       "      <td>1.443300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>159</td>\n",
       "      <td>1.587300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>160</td>\n",
       "      <td>1.573600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>161</td>\n",
       "      <td>1.689000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>162</td>\n",
       "      <td>1.568900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>163</td>\n",
       "      <td>1.482300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>164</td>\n",
       "      <td>1.656200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>165</td>\n",
       "      <td>1.602300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>166</td>\n",
       "      <td>1.518300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>167</td>\n",
       "      <td>1.369700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>168</td>\n",
       "      <td>1.602400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>169</td>\n",
       "      <td>1.457600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>170</td>\n",
       "      <td>1.467800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>171</td>\n",
       "      <td>1.537000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>172</td>\n",
       "      <td>1.507800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>173</td>\n",
       "      <td>1.667200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>174</td>\n",
       "      <td>1.413800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>175</td>\n",
       "      <td>1.495500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>176</td>\n",
       "      <td>1.520100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>177</td>\n",
       "      <td>1.484000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>178</td>\n",
       "      <td>1.560000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>179</td>\n",
       "      <td>1.403100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>180</td>\n",
       "      <td>1.562900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>181</td>\n",
       "      <td>1.503600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>182</td>\n",
       "      <td>1.546900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>183</td>\n",
       "      <td>1.580100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>184</td>\n",
       "      <td>1.415300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>185</td>\n",
       "      <td>1.513400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>186</td>\n",
       "      <td>1.579100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>187</td>\n",
       "      <td>1.415400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>188</td>\n",
       "      <td>1.553900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
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      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800000; text-decoration-color: #800000\">╭─────────────────────────────── </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">Traceback </span><span style=\"color: #bf7f7f; text-decoration-color: #bf7f7f; font-weight: bold\">(most recent call last)</span><span style=\"color: #800000; text-decoration-color: #800000\"> ────────────────────────────────╮</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">&lt;cell line: 46&gt;</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">46</span>                                                                            <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.9/dist-packages/transformers/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">trainer.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">1628</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">train</span>                     <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1625 │   │   </span>inner_training_loop = find_executable_batch_size(                                 <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1626 │   │   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._inner_training_loop, <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._train_batch_size, args.auto_find_batch_size  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1627 │   │   </span>)                                                                                 <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>1628 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> inner_training_loop(                                                       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1629 │   │   │   </span>args=args,                                                                    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1630 │   │   │   </span>resume_from_checkpoint=resume_from_checkpoint,                                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1631 │   │   │   </span>trial=trial,                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.9/dist-packages/transformers/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">trainer.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">1895</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">_inner_training_loop</span>      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1892 │   │   │   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">with</span> model.no_sync():                                                 <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1893 │   │   │   │   │   │   </span>tr_loss_step = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.training_step(model, inputs)                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1894 │   │   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">else</span>:                                                                     <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>1895 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   │   </span>tr_loss_step = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.training_step(model, inputs)                      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1896 │   │   │   │   </span>                                                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1897 │   │   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> (                                                                      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1898 │   │   │   │   │   </span>args.logging_nan_inf_filter                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.9/dist-packages/transformers/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">trainer.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">2647</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">training_step</span>             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2644 │   │   │   </span>loss = loss / <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.args.gradient_accumulation_steps                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2645 │   │   </span>                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2646 │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.do_grad_scaling:                                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>2647 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.scaler.scale(loss).backward()                                            <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2648 │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">elif</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.use_apex:                                                               <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2649 │   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">with</span> amp.scale_loss(loss, <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.optimizer) <span style=\"color: #0000ff; text-decoration-color: #0000ff\">as</span> scaled_loss:                     <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2650 │   │   │   │   </span>scaled_loss.backward()                                                    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.9/dist-packages/torch/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">_tensor.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">487</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">backward</span>                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 484 │   │   │   │   </span>create_graph=create_graph,                                                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 485 │   │   │   │   </span>inputs=inputs,                                                            <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 486 │   │   │   </span>)                                                                             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span> 487 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>torch.autograd.backward(                                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 488 │   │   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>, gradient, retain_graph, create_graph, inputs=inputs                     <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 489 │   │   </span>)                                                                                 <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 490 </span>                                                                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.9/dist-packages/torch/autograd/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">__init__.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">200</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">backward</span>                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">197 │   # The reason we repeat same the comment below is that</span>                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">198 │   # some Python versions print out the first line of a multi-line function</span>               <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">199 │   # calls in the traceback and some print out the last line</span>                              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>200 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span>Variable._execution_engine.run_backward(  <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"># Calls into the C++ engine to run the bac</span>   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">201 │   │   </span>tensors, grad_tensors_, retain_graph, create_graph, inputs,                        <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">202 │   │   </span>allow_unreachable=<span style=\"color: #0000ff; text-decoration-color: #0000ff\">True</span>, accumulate_grad=<span style=\"color: #0000ff; text-decoration-color: #0000ff\">True</span>)  <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"># Calls into the C++ engine to ru</span>   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">203 </span>                                                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">╰──────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
       "<span style=\"color: #ff0000; text-decoration-color: #ff0000; font-weight: bold\">KeyboardInterrupt</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[31m╭─\u001b[0m\u001b[31m──────────────────────────────\u001b[0m\u001b[31m \u001b[0m\u001b[1;31mTraceback \u001b[0m\u001b[1;2;31m(most recent call last)\u001b[0m\u001b[31m \u001b[0m\u001b[31m───────────────────────────────\u001b[0m\u001b[31m─╮\u001b[0m\n",
       "\u001b[31m│\u001b[0m in \u001b[92m<cell line: 46>\u001b[0m:\u001b[94m46\u001b[0m                                                                            \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.9/dist-packages/transformers/\u001b[0m\u001b[1;33mtrainer.py\u001b[0m:\u001b[94m1628\u001b[0m in \u001b[92mtrain\u001b[0m                     \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1625 \u001b[0m\u001b[2m│   │   \u001b[0minner_training_loop = find_executable_batch_size(                                 \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1626 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[96mself\u001b[0m._inner_training_loop, \u001b[96mself\u001b[0m._train_batch_size, args.auto_find_batch_size  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1627 \u001b[0m\u001b[2m│   │   \u001b[0m)                                                                                 \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m1628 \u001b[2m│   │   \u001b[0m\u001b[94mreturn\u001b[0m inner_training_loop(                                                       \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1629 \u001b[0m\u001b[2m│   │   │   \u001b[0margs=args,                                                                    \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1630 \u001b[0m\u001b[2m│   │   │   \u001b[0mresume_from_checkpoint=resume_from_checkpoint,                                \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1631 \u001b[0m\u001b[2m│   │   │   \u001b[0mtrial=trial,                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.9/dist-packages/transformers/\u001b[0m\u001b[1;33mtrainer.py\u001b[0m:\u001b[94m1895\u001b[0m in \u001b[92m_inner_training_loop\u001b[0m      \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1892 \u001b[0m\u001b[2m│   │   │   │   │   \u001b[0m\u001b[94mwith\u001b[0m model.no_sync():                                                 \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1893 \u001b[0m\u001b[2m│   │   │   │   │   │   \u001b[0mtr_loss_step = \u001b[96mself\u001b[0m.training_step(model, inputs)                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1894 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m\u001b[94melse\u001b[0m:                                                                     \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m1895 \u001b[2m│   │   │   │   │   \u001b[0mtr_loss_step = \u001b[96mself\u001b[0m.training_step(model, inputs)                      \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1896 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m                                                                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1897 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m\u001b[94mif\u001b[0m (                                                                      \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1898 \u001b[0m\u001b[2m│   │   │   │   │   \u001b[0margs.logging_nan_inf_filter                                           \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.9/dist-packages/transformers/\u001b[0m\u001b[1;33mtrainer.py\u001b[0m:\u001b[94m2647\u001b[0m in \u001b[92mtraining_step\u001b[0m             \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m2644 \u001b[0m\u001b[2m│   │   │   \u001b[0mloss = loss / \u001b[96mself\u001b[0m.args.gradient_accumulation_steps                           \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m2645 \u001b[0m\u001b[2m│   │   \u001b[0m                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m2646 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mif\u001b[0m \u001b[96mself\u001b[0m.do_grad_scaling:                                                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m2647 \u001b[2m│   │   │   \u001b[0m\u001b[96mself\u001b[0m.scaler.scale(loss).backward()                                            \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m2648 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94melif\u001b[0m \u001b[96mself\u001b[0m.use_apex:                                                               \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m2649 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[94mwith\u001b[0m amp.scale_loss(loss, \u001b[96mself\u001b[0m.optimizer) \u001b[94mas\u001b[0m scaled_loss:                     \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m2650 \u001b[0m\u001b[2m│   │   │   │   \u001b[0mscaled_loss.backward()                                                    \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.9/dist-packages/torch/\u001b[0m\u001b[1;33m_tensor.py\u001b[0m:\u001b[94m487\u001b[0m in \u001b[92mbackward\u001b[0m                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m 484 \u001b[0m\u001b[2m│   │   │   │   \u001b[0mcreate_graph=create_graph,                                                \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m 485 \u001b[0m\u001b[2m│   │   │   │   \u001b[0minputs=inputs,                                                            \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m 486 \u001b[0m\u001b[2m│   │   │   \u001b[0m)                                                                             \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 487 \u001b[2m│   │   \u001b[0mtorch.autograd.backward(                                                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m 488 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[96mself\u001b[0m, gradient, retain_graph, create_graph, inputs=inputs                     \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m 489 \u001b[0m\u001b[2m│   │   \u001b[0m)                                                                                 \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m 490 \u001b[0m                                                                                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.9/dist-packages/torch/autograd/\u001b[0m\u001b[1;33m__init__.py\u001b[0m:\u001b[94m200\u001b[0m in \u001b[92mbackward\u001b[0m                \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m197 \u001b[0m\u001b[2m│   \u001b[0m\u001b[2m# The reason we repeat same the comment below is that\u001b[0m                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m198 \u001b[0m\u001b[2m│   \u001b[0m\u001b[2m# some Python versions print out the first line of a multi-line function\u001b[0m               \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m199 \u001b[0m\u001b[2m│   \u001b[0m\u001b[2m# calls in the traceback and some print out the last line\u001b[0m                              \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m200 \u001b[2m│   \u001b[0mVariable._execution_engine.run_backward(  \u001b[2m# Calls into the C++ engine to run the bac\u001b[0m   \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m201 \u001b[0m\u001b[2m│   │   \u001b[0mtensors, grad_tensors_, retain_graph, create_graph, inputs,                        \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m202 \u001b[0m\u001b[2m│   │   \u001b[0mallow_unreachable=\u001b[94mTrue\u001b[0m, accumulate_grad=\u001b[94mTrue\u001b[0m)  \u001b[2m# Calls into the C++ engine to ru\u001b[0m   \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m203 \u001b[0m                                                                                           \u001b[31m│\u001b[0m\n",
       "\u001b[31m╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n",
       "\u001b[1;91mKeyboardInterrupt\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "def generate_prompt(data_point):\n",
    "    # sorry about the formatting disaster gotta move fast\n",
    "    if data_point[\"input\"]:\n",
    "        return f\"\"\"### Instruction:\n",
    "{data_point[\"instruction\"]}\n",
    "### Input:\n",
    "{data_point[\"input\"]}\n",
    "### Response:\n",
    "{data_point[\"output\"]}\"\"\"\n",
    "    else:\n",
    "        return f\"\"\"### Instruction:\n",
    "{data_point[\"instruction\"]}\n",
    "### Response:\n",
    "{data_point[\"output\"]}\"\"\"\n",
    "\n",
    "\n",
    "data = data.shuffle().map(\n",
    "    lambda data_point: tokenizer(\n",
    "        generate_prompt(data_point),\n",
    "        truncation=False,\n",
    "        padding='longest',\n",
    "    )\n",
    ")\n",
    "\n",
    "\n",
    "trainer = transformers.Trainer(\n",
    "    model=model,\n",
    "    train_dataset=data[\"train\"],\n",
    "    args=transformers.TrainingArguments(\n",
    "        per_device_train_batch_size=MICRO_BATCH_SIZE,\n",
    "        gradient_accumulation_steps=GRADIENT_ACCUMULATION_STEPS,\n",
    "        warmup_steps=100,\n",
    "        num_train_epochs=EPOCHS,\n",
    "        learning_rate=LEARNING_RATE,\n",
    "        fp16=True,\n",
    "        logging_steps=1,\n",
    "        output_dir=\"lora-smartscraper\",\n",
    "        save_total_limit=3,\n",
    "    ),\n",
    "    data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),\n",
    ")\n",
    "model.config.use_cache = False\n",
    "trainer.train(resume_from_checkpoint=False)\n",
    "\n",
    "model.save_pretrained(\"lora-smartscraper\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 359,
     "referenced_widgets": [
      "81c703e0e25b4881ad29f212fbfdf3cb",
      "945c4d35a1594bf7aee51bb969af430a",
      "ba0df180bcc344959f03de6d4f2e0d71",
      "741c4c771441436d8aada6953dc81a21",
      "c2378fc423414cc59b9581bda59ab10f",
      "4480e1f1de7e4ab4bf8395d266c9c5eb",
      "1724696180b94dff8205c6d9daa88e5e",
      "19ec162e98dc45f1ae8057c82b935d99",
      "f1da3ec65adc4567972f79c268fd4a9a",
      "3499bdf15f724dd49aeca181da853884",
      "89ec47ce95304a0aaad6a79b9931c2cb",
      "aaf9974947634dd082b39da0e68b9e18",
      "97754d068a234bf8a72171bdaef6a216",
      "1086e781817b4915844af4eacc0c1990",
      "63ecc2d944434540af0afe8736b78cac",
      "280874328c6743b9a07ec7d9eabcdc4c",
      "c60829f538d04576ae5e899a61cadb6d"
     ]
    },
    "id": "DpYr24pR8T_0",
    "outputId": "661ce0e8-1fb9-47fd-c663-7ca8f5990d27"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Token is valid.\n",
      "Your token has been saved in your configured git credential helpers (store).\n",
      "Your token has been saved to /root/.cache/huggingface/token\n",
      "Login successful\n"
     ]
    }
   ],
   "source": [
    "from huggingface_hub import notebook_login\n",
    "\n",
    "notebook_login()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 155,
     "referenced_widgets": [
      "7dfec0e6b4f2402a82ba007115ba1464",
      "2e72e33bec9c4ef988cff24d45f23846",
      "bd7b885019e543d7a81cb458a5a0937f",
      "afbd48d08a344b0aa4c07717976d2860",
      "20c10897b0db41e9b20124be03fc8d31",
      "00a6d86d749348dbb0eafe9ca57945ae",
      "1982c8a9b9d245c687ed85f46d433bb6",
      "303ce05799864328ba14c6bdc963b63c",
      "0281cda4e36e456ea5570d79a35b6725",
      "de9fa2b33ffd4969808c4112c365320f",
      "2d468349b6fc41539fe6a053b09acbb2",
      "08b4848ba9f14e0ebea2606918aa9ced",
      "4e2aaec821e343aaaabb446d90f802b4",
      "9c3b4516d9534c4d95d67661c49d7223",
      "5b1b416f29c247abb450247139e851a2",
      "8aa5f7d1d1074941904fe57345358a30",
      "c7b75d35ef0c4be8bb176655c367f240",
      "03d9568f934245adb50e3066dbb38163",
      "1a6f7c782d054a62854b2e7e05bfb655",
      "020d1b155aff41b38dd12f986f4a3ea7",
      "d48ed2e64a5b466f90fa57f72ea76046",
      "784db46ed6bf4c35929cf9cbffa112c0"
     ]
    },
    "id": "VxB6UV5XAvvP",
    "outputId": "7cd82ca3-8f18-4b60-b5bb-8225683a9014"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7dfec0e6b4f2402a82ba007115ba1464",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "adapter_model.bin:   0%|          | 0.00/8.43M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "08b4848ba9f14e0ebea2606918aa9ced",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Upload 1 LFS files:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "CommitInfo(commit_url='https://huggingface.co/DDDano333/smartscraper/commit/83a671b384ebf41ac421507590596c2d3da11789', commit_message='Upload model', commit_description='', oid='83a671b384ebf41ac421507590596c2d3da11789', pr_url=None, pr_revision=None, pr_num=None)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.push_to_hub(\"DDDano333/smartscraper\", use_auth_token=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CqfJCzzPxvaQ"
   },
   "source": [
    "## Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "453IQIGm9B02"
   },
   "outputs": [],
   "source": [
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 385
    },
    "id": "qgK1e-ZbweHL",
    "outputId": "53a4aabe-f038-4d7f-df07-6d2a22eee0b1"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Overriding torch_dtype=None with `torch_dtype=torch.float16` due to requirements of `bitsandbytes` to enable model loading in mixed int8. Either pass torch_dtype=torch.float16 or don't pass this argument at all to remove this warning.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800000; text-decoration-color: #800000\">╭─────────────────────────────── </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">Traceback </span><span style=\"color: #bf7f7f; text-decoration-color: #bf7f7f; font-weight: bold\">(most recent call last)</span><span style=\"color: #800000; text-decoration-color: #800000\"> ────────────────────────────────╮</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">&lt;cell line: 6&gt;</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">6</span>                                                                              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.9/dist-packages/transformers/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">modeling_utils.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">2578</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">from_pretrained</span>    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2575 │   │   │   │   │   </span>key: device_map[key] <span style=\"color: #0000ff; text-decoration-color: #0000ff\">for</span> key <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">in</span> device_map.keys() <span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> key <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">not</span> <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">in</span> modu  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2576 │   │   │   │   </span>}                                                                         <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2577 │   │   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> <span style=\"color: #808000; text-decoration-color: #808000\">\"cpu\"</span> <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">in</span> device_map_without_lm_head.values() <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> <span style=\"color: #808000; text-decoration-color: #808000\">\"disk\"</span> <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">in</span> device_map_  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>2578 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">raise</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">ValueError</span>(                                                     <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2579 </span><span style=\"color: #bfbfbf; text-decoration-color: #bfbfbf\">│   │   │   │   │   │   </span><span style=\"color: #808000; text-decoration-color: #808000\">\"\"\"</span>                                                               <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2580 </span><span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">│   │   │   │   │   │   </span><span style=\"color: #808000; text-decoration-color: #808000\">Some modules are dispatched on the CPU or the disk. Make sure yo</span>  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2581 </span><span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">│   │   │   │   │   │   </span><span style=\"color: #808000; text-decoration-color: #808000\">the quantized model. If you have set a value for `max_memory` yo</span>  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">╰──────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
       "<span style=\"color: #ff0000; text-decoration-color: #ff0000; font-weight: bold\">ValueError: </span>\n",
       "                        Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to \n",
       "fit\n",
       "                        the quantized model. If you have set a value for `max_memory` you should increase that. To \n",
       "have\n",
       "                        an idea of the modules that are set on the CPU or RAM you can print model.hf_device_map.\n",
       "                        \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[31m╭─\u001b[0m\u001b[31m──────────────────────────────\u001b[0m\u001b[31m \u001b[0m\u001b[1;31mTraceback \u001b[0m\u001b[1;2;31m(most recent call last)\u001b[0m\u001b[31m \u001b[0m\u001b[31m───────────────────────────────\u001b[0m\u001b[31m─╮\u001b[0m\n",
       "\u001b[31m│\u001b[0m in \u001b[92m<cell line: 6>\u001b[0m:\u001b[94m6\u001b[0m                                                                              \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.9/dist-packages/transformers/\u001b[0m\u001b[1;33mmodeling_utils.py\u001b[0m:\u001b[94m2578\u001b[0m in \u001b[92mfrom_pretrained\u001b[0m    \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m2575 \u001b[0m\u001b[2m│   │   │   │   │   \u001b[0mkey: device_map[key] \u001b[94mfor\u001b[0m key \u001b[95min\u001b[0m device_map.keys() \u001b[94mif\u001b[0m key \u001b[95mnot\u001b[0m \u001b[95min\u001b[0m modu  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m2576 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m}                                                                         \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m2577 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m\u001b[94mif\u001b[0m \u001b[33m\"\u001b[0m\u001b[33mcpu\u001b[0m\u001b[33m\"\u001b[0m \u001b[95min\u001b[0m device_map_without_lm_head.values() \u001b[95mor\u001b[0m \u001b[33m\"\u001b[0m\u001b[33mdisk\u001b[0m\u001b[33m\"\u001b[0m \u001b[95min\u001b[0m device_map_  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m2578 \u001b[2m│   │   │   │   │   \u001b[0m\u001b[94mraise\u001b[0m \u001b[96mValueError\u001b[0m(                                                     \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m2579 \u001b[0m\u001b[2;90m│   │   │   │   │   │   \u001b[0m\u001b[33m\"\"\"\u001b[0m                                                               \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m2580 \u001b[0m\u001b[2;33m│   │   │   │   │   │   \u001b[0m\u001b[33mSome modules are dispatched on the CPU or the disk. Make sure yo\u001b[0m  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m2581 \u001b[0m\u001b[2;33m│   │   │   │   │   │   \u001b[0m\u001b[33mthe quantized model. If you have set a value for `max_memory` yo\u001b[0m  \u001b[31m│\u001b[0m\n",
       "\u001b[31m╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n",
       "\u001b[1;91mValueError: \u001b[0m\n",
       "                        Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to \n",
       "fit\n",
       "                        the quantized model. If you have set a value for `max_memory` you should increase that. To \n",
       "have\n",
       "                        an idea of the modules that are set on the CPU or RAM you can print model.hf_device_map.\n",
       "                        \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from peft import PeftModel\n",
    "from transformers import LLaMATokenizer, LLaMAForCausalLM, GenerationConfig\n",
    "\n",
    "tokenizer = LLaMATokenizer.from_pretrained(\"decapoda-research/llama-7b-hf\")\n",
    "\n",
    "model = LLaMAForCausalLM.from_pretrained(\n",
    "    \"decapoda-research/llama-7b-hf\",\n",
    "    load_in_8bit=True,\n",
    "    device_map=\"auto\",\n",
    ")\n",
    "model = PeftModel.from_pretrained(model, \"DDDano333/smartscraper\")\n",
    "# model = get_peft_model(model, config)\n",
    "\n",
    "PROMPT = \"\"\"### Below is an instruction that describes a task. Write a response that appropriately completes the request.\n",
    "### Instruction: Please parse the given text and extract the ticket information. Create a list of dictionaries, where each dictionary represents a single ticket and contains the following keys: 'ticket_number', 'court', 'agency', 'violation_date', and 'status'. Convert the extracted information into the appropriate format and output the resulting list of dictionaries.\n",
    "### Input: [Header]Traffic Ticket Information Center\\nTicket Resolution Process:\\nI.User Terms of Service\\nII.Fetch Ticket Information\\nIII.Online Citation Options\\nIV.Fee and Appointment\\nV.Approval\\nDL Number: MZQ 834\\nDate of Birth: 03/24/1939\\nClick on Ticket Number for more details.\\nTicket NumberCourtLaw Enforcement AgencyViolation DateStatus\\n\\n2566466Jonesview Legal ComplexGoodman PLC04-12-2023Appeal Granted\\n2313665Michaelfurt Courthouse AnnexWalton-Arnold02 23 2023Continuance Granted\\n9033403East Troyville Tribunal HousePowell, Shields and Daniel03/24/2023Court Date Set\\n4588574Robertsside Court FacilityLarson Group03-07-2023Reopened\\n7064063Teresamouth Court ChambersLi LLC01/09/2023Appeal Granted\\n5195664New Timothy Tribunal HouseKane-Sanders02-12-2023Appeal Filed\\n5435899Cynthiaport Judiciary PremisesFisher-Maxwell04-03-2023Sentenced\\n6667788Miguelborough Court CenterTorres, Harrison and Bishop01/19/2023Guilty\\n[Warnings and Instructions]\\nBe aware that it can take up to 30 days for traffic tickets to be posted online. If you're unable to find your ticket after this period, reach out to the law enforcement agency that issued the ticket. You are still required to address your ticket before the deadline, even if it's not available online. If you've already reached out to the agency and your ticket is still not online, visit the courthouse listed on your ticket by the due date. Remember, neglecting to handle your ticket in a timely manner may result in penalties.\n",
    "### Response:\"\"\"\n",
    "\n",
    "inputs = tokenizer(\n",
    "    PROMPT,\n",
    "    return_tensors=\"pt\",\n",
    ")\n",
    "input_ids = inputs[\"input_ids\"].cuda()\n",
    "\n",
    "generation_config = GenerationConfig(\n",
    "    temperature=0.6,\n",
    "    top_p=0.95,\n",
    "    repetition_penalty=1.15,\n",
    ")\n",
    "print(\"Generating...\")\n",
    "generation_output = model.generate(\n",
    "    input_ids=input_ids,\n",
    "    generation_config=generation_config,\n",
    "    return_dict_in_generate=True,\n",
    "    output_scores=True,\n",
    "    max_new_tokens=128,\n",
    ")\n",
    "for s in generation_output.sequences:\n",
    "    print(tokenizer.decode(s))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "gS7f5biax4qV"
   },
   "outputs": [],
   "source": [
    "PROMPT ='''### instruction: Please parse the given text and extract the ticket information. Create a list of dictionaries, where each dictionary represents a single ticket and contains the following keys: 'ticket_number', 'court', 'agency', 'violation_date', and 'status'. Convert the extracted information into the appropriate format and output the resulting list of dictionaries.\n",
    "### input: [Header]Traffic Ticket Status\\nTicket Resolution Steps:\\n- Terms of Use\\n- Obtain Ticket Info\\n- Internet Ticket Alternatives\\n- Payment and Booking\\n- Acknowledgment\\n\\nDL Number: 3800 YQ\\nBirth Date: 11/02/1987\\n\\nClick the Ticket Number for more information.\\n\\nTicket NumberCourtLaw Enforcement AgencyViolation DateStatus\\n\\n7833537West Karen Tribunal HouseEllis-Calderon03 01 2023Appeal Filed\\n1517406Johnland Judicial BuildingElliott Group03-01-2023Reopened\\n5508653Longbury Judicial CenterNguyen-Holmes02/24/2023Extension\\n8926090Lake Mark Court FacilityWarner Inc03-07-2023Adjourned\\nMake sure to review your ticket information before proceeding.\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "YJYi3pdNemBn",
    "outputId": "39aa2d19-0255-4708-e3d7-25c45497cc3d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800000; text-decoration-color: #800000\">╭─────────────────────────────── </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">Traceback </span><span style=\"color: #bf7f7f; text-decoration-color: #bf7f7f; font-weight: bold\">(most recent call last)</span><span style=\"color: #800000; text-decoration-color: #800000\"> ────────────────────────────────╮</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.9/dist-packages/IPython/core/magics/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">execution.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">1335</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">time</span>             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1332 │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">else</span>:                                                                             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1333 │   │   │   </span>st = clock2()                                                                 <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1334 │   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">try</span>:                                                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>1335 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span>exec(code, glob, local_ns)                                                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1336 │   │   │   │   </span>out=<span style=\"color: #0000ff; text-decoration-color: #0000ff\">None</span>                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1337 │   │   │   │   # multi-line %%time case</span>                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1338 │   │   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> expr_val <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">is</span> <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">not</span> <span style=\"color: #0000ff; text-decoration-color: #0000ff\">None</span>:                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">&lt;module&gt;</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">13</span>                                                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.9/dist-packages/peft/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">peft_model.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">721</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">generate</span>                        <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 718 │   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.base_model.prepare_inputs_for_generation = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.prepare_inputs_for_generati  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 719 │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">try</span>:                                                                              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 720 │   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">not</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">isinstance</span>(peft_config, PromptLearningConfig):                         <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span> 721 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span>outputs = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.base_model.generate(**kwargs)                              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 722 │   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">else</span>:                                                                         <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 723 │   │   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> <span style=\"color: #808000; text-decoration-color: #808000\">\"input_ids\"</span> <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">not</span> <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">in</span> kwargs:                                             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 724 │   │   │   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">raise</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">ValueError</span>(<span style=\"color: #808000; text-decoration-color: #808000\">\"input_ids must be provided for Peft model generati</span>  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.9/dist-packages/torch/utils/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">_contextlib.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">115</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">decorate_context</span>        <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">112 │   </span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff; font-weight: bold\">@functools</span>.wraps(func)                                                                 <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">113 │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">def</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">decorate_context</span>(*args, **kwargs):                                                 <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">114 │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">with</span> ctx_factory():                                                                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>115 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> func(*args, **kwargs)                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">116 │   </span>                                                                                       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">117 │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> decorate_context                                                                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">118 </span>                                                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.9/dist-packages/transformers/generation/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">utils.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">1406</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">generate</span>         <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1403 │   │   │   │   </span>)                                                                         <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1404 │   │   │   </span>                                                                              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1405 │   │   │   # 11. run greedy search</span>                                                       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>1406 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.greedy_search(                                                    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1407 │   │   │   │   </span>input_ids,                                                                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1408 │   │   │   │   </span>logits_processor=logits_processor,                                        <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1409 │   │   │   │   </span>stopping_criteria=stopping_criteria,                                      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.9/dist-packages/transformers/generation/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">utils.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">2201</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">greedy_search</span>    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2198 │   │   │   </span>model_inputs = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.prepare_inputs_for_generation(input_ids, **model_kwargs)  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2199 │   │   │   </span>                                                                              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2200 │   │   │   # forward pass to get next token</span>                                              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>2201 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>outputs = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>(                                                               <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2202 │   │   │   │   </span>**model_inputs,                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2203 │   │   │   │   </span>return_dict=<span style=\"color: #0000ff; text-decoration-color: #0000ff\">True</span>,                                                         <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2204 │   │   │   │   </span>output_attentions=output_attentions,                                      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.9/dist-packages/torch/nn/modules/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">module.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">1501</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">_call_impl</span>             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1498 │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">not</span> (<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._backward_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._backward_pre_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._forward_hooks   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1499 │   │   │   │   </span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> _global_backward_pre_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> _global_backward_hooks                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1500 │   │   │   │   </span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> _global_forward_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> _global_forward_pre_hooks):                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>1501 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> forward_call(*args, **kwargs)                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1502 │   │   # Do not call functions when jit is used</span>                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1503 │   │   </span>full_backward_hooks, non_full_backward_hooks = [], []                             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1504 │   │   </span>backward_pre_hooks = []                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.9/dist-packages/accelerate/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">hooks.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">165</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">new_forward</span>                    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">162 │   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">with</span> torch.no_grad():                                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">163 │   │   │   │   </span>output = old_forward(*args, **kwargs)                                      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">164 │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">else</span>:                                                                              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>165 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>output = old_forward(*args, **kwargs)                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">166 │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> module._hf_hook.post_forward(module, output)                                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">167 │   </span>                                                                                       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">168 │   </span>module.forward = new_forward                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.9/dist-packages/transformers/models/llama/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">modeling_llama.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">770</span> in        <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">forward</span>                                                                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">767 │   │   </span>return_dict = return_dict <span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> return_dict <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">is</span> <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">not</span> <span style=\"color: #0000ff; text-decoration-color: #0000ff\">None</span> <span style=\"color: #0000ff; text-decoration-color: #0000ff\">else</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.config.use_return   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">768 │   │   </span>                                                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">769 │   │   # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)</span>    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>770 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>outputs = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.model(                                                              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">771 │   │   │   </span>input_ids=input_ids,                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">772 │   │   │   </span>attention_mask=attention_mask,                                                 <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">773 │   │   │   </span>past_key_values=past_key_values,                                               <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.9/dist-packages/torch/nn/modules/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">module.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">1501</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">_call_impl</span>             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1498 │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">not</span> (<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._backward_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._backward_pre_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._forward_hooks   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1499 │   │   │   │   </span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> _global_backward_pre_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> _global_backward_hooks                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1500 │   │   │   │   </span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> _global_forward_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> _global_forward_pre_hooks):                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>1501 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> forward_call(*args, **kwargs)                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1502 │   │   # Do not call functions when jit is used</span>                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1503 │   │   </span>full_backward_hooks, non_full_backward_hooks = [], []                             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1504 │   │   </span>backward_pre_hooks = []                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.9/dist-packages/accelerate/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">hooks.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">165</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">new_forward</span>                    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">162 │   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">with</span> torch.no_grad():                                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">163 │   │   │   │   </span>output = old_forward(*args, **kwargs)                                      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">164 │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">else</span>:                                                                              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>165 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>output = old_forward(*args, **kwargs)                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">166 │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> module._hf_hook.post_forward(module, output)                                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">167 │   </span>                                                                                       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">168 │   </span>module.forward = new_forward                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.9/dist-packages/transformers/models/llama/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">modeling_llama.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">619</span> in        <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">forward</span>                                                                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">616 │   │   │   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">None</span>,                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">617 │   │   │   │   </span>)                                                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">618 │   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">else</span>:                                                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>619 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span>layer_outputs = decoder_layer(                                             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">620 │   │   │   │   │   </span>hidden_states,                                                         <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">621 │   │   │   │   │   </span>attention_mask=attention_mask,                                         <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">622 │   │   │   │   │   </span>past_key_value=past_key_value,                                         <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.9/dist-packages/torch/nn/modules/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">module.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">1501</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">_call_impl</span>             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1498 │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">not</span> (<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._backward_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._backward_pre_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._forward_hooks   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1499 │   │   │   │   </span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> _global_backward_pre_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> _global_backward_hooks                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1500 │   │   │   │   </span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> _global_forward_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> _global_forward_pre_hooks):                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>1501 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> forward_call(*args, **kwargs)                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1502 │   │   # Do not call functions when jit is used</span>                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1503 │   │   </span>full_backward_hooks, non_full_backward_hooks = [], []                             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1504 │   │   </span>backward_pre_hooks = []                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.9/dist-packages/accelerate/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">hooks.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">165</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">new_forward</span>                    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">162 │   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">with</span> torch.no_grad():                                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">163 │   │   │   │   </span>output = old_forward(*args, **kwargs)                                      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">164 │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">else</span>:                                                                              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>165 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>output = old_forward(*args, **kwargs)                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">166 │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> module._hf_hook.post_forward(module, output)                                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">167 │   </span>                                                                                       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">168 │   </span>module.forward = new_forward                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.9/dist-packages/transformers/models/llama/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">modeling_llama.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">316</span> in        <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">forward</span>                                                                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">313 │   │   </span>hidden_states = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.input_layernorm(hidden_states)                                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">314 │   │   </span>                                                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">315 │   │   # Self Attention</span>                                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>316 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>hidden_states, self_attn_weights, present_key_value = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.self_attn(              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">317 │   │   │   </span>hidden_states=hidden_states,                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">318 │   │   │   </span>past_key_value=past_key_value,                                                 <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">319 │   │   │   </span>attention_mask=attention_mask,                                                 <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.9/dist-packages/torch/nn/modules/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">module.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">1501</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">_call_impl</span>             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1498 │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">not</span> (<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._backward_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._backward_pre_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._forward_hooks   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1499 │   │   │   │   </span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> _global_backward_pre_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> _global_backward_hooks                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1500 │   │   │   │   </span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> _global_forward_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> _global_forward_pre_hooks):                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>1501 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> forward_call(*args, **kwargs)                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1502 │   │   # Do not call functions when jit is used</span>                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1503 │   │   </span>full_backward_hooks, non_full_backward_hooks = [], []                             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1504 │   │   </span>backward_pre_hooks = []                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.9/dist-packages/accelerate/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">hooks.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">165</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">new_forward</span>                    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">162 │   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">with</span> torch.no_grad():                                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">163 │   │   │   │   </span>output = old_forward(*args, **kwargs)                                      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">164 │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">else</span>:                                                                              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>165 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>output = old_forward(*args, **kwargs)                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">166 │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> module._hf_hook.post_forward(module, output)                                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">167 │   </span>                                                                                       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">168 │   </span>module.forward = new_forward                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.9/dist-packages/transformers/models/llama/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">modeling_llama.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">216</span> in        <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">forward</span>                                                                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">213 │   │   </span>                                                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">214 │   │   </span>bsz, q_len, _ = hidden_states.size()                                               <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">215 │   │   </span>                                                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>216 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>query_states = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.q_proj(hidden_states).view(bsz, q_len, <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.num_heads, <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">217 │   │   </span>key_states = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.k_proj(hidden_states).view(bsz, q_len, <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.num_heads, <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.he   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">218 │   │   </span>value_states = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.v_proj(hidden_states).view(bsz, q_len, <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.num_heads, <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">219 </span>                                                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.9/dist-packages/torch/nn/modules/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">module.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">1501</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">_call_impl</span>             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1498 │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">not</span> (<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._backward_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._backward_pre_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._forward_hooks   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1499 │   │   │   │   </span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> _global_backward_pre_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> _global_backward_hooks                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1500 │   │   │   │   </span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> _global_forward_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> _global_forward_pre_hooks):                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>1501 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> forward_call(*args, **kwargs)                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1502 │   │   # Do not call functions when jit is used</span>                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1503 │   │   </span>full_backward_hooks, non_full_backward_hooks = [], []                             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1504 │   │   </span>backward_pre_hooks = []                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.9/dist-packages/peft/tuners/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">lora.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">557</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">forward</span>                        <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">554 │   │   │   │   │   │   </span>x = x.float()                                                      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">555 │   │   │   │   │   </span>output = (                                                             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">556 │   │   │   │   │   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.lora_B[<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.active_adapter](                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>557 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   │   │   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.lora_A[<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.active_adapter](<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.lora_dropout[<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.acti   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">558 │   │   │   │   │   │   </span>).to(expected_dtype)                                               <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">559 │   │   │   │   │   │   </span>* <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.scaling[<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.active_adapter]                                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">560 │   │   │   │   │   </span>)                                                                      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.9/dist-packages/torch/nn/modules/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">module.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">1501</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">_call_impl</span>             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1498 │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">not</span> (<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._backward_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._backward_pre_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._forward_hooks   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1499 │   │   │   │   </span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> _global_backward_pre_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> _global_backward_hooks                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1500 │   │   │   │   </span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> _global_forward_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> _global_forward_pre_hooks):                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>1501 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> forward_call(*args, **kwargs)                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1502 │   │   # Do not call functions when jit is used</span>                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1503 │   │   </span>full_backward_hooks, non_full_backward_hooks = [], []                             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1504 │   │   </span>backward_pre_hooks = []                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.9/dist-packages/torch/nn/modules/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">linear.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">114</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">forward</span>                 <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">111 │   │   │   </span>init.uniform_(<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.bias, -bound, bound)                                        <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">112 │   </span>                                                                                       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">113 │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">def</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">forward</span>(<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>, <span style=\"color: #00ffff; text-decoration-color: #00ffff\">input</span>: Tensor) -&gt; Tensor:                                            <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>114 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> F.linear(<span style=\"color: #00ffff; text-decoration-color: #00ffff\">input</span>, <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.weight, <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.bias)                                     <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">115 │   </span>                                                                                       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">116 │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">def</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">extra_repr</span>(<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>) -&gt; <span style=\"color: #00ffff; text-decoration-color: #00ffff\">str</span>:                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">117 │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> <span style=\"color: #808000; text-decoration-color: #808000\">'in_features={}, out_features={}, bias={}'</span>.format(                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">╰──────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
       "<span style=\"color: #ff0000; text-decoration-color: #ff0000; font-weight: bold\">RuntimeError: </span>expected scalar type Float but found Half\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[31m╭─\u001b[0m\u001b[31m──────────────────────────────\u001b[0m\u001b[31m \u001b[0m\u001b[1;31mTraceback \u001b[0m\u001b[1;2;31m(most recent call last)\u001b[0m\u001b[31m \u001b[0m\u001b[31m───────────────────────────────\u001b[0m\u001b[31m─╮\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.9/dist-packages/IPython/core/magics/\u001b[0m\u001b[1;33mexecution.py\u001b[0m:\u001b[94m1335\u001b[0m in \u001b[92mtime\u001b[0m             \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1332 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94melse\u001b[0m:                                                                             \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1333 \u001b[0m\u001b[2m│   │   │   \u001b[0mst = clock2()                                                                 \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1334 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[94mtry\u001b[0m:                                                                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m1335 \u001b[2m│   │   │   │   \u001b[0mexec(code, glob, local_ns)                                                \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1336 \u001b[0m\u001b[2m│   │   │   │   \u001b[0mout=\u001b[94mNone\u001b[0m                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1337 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m\u001b[2m# multi-line %%time case\u001b[0m                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1338 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m\u001b[94mif\u001b[0m expr_val \u001b[95mis\u001b[0m \u001b[95mnot\u001b[0m \u001b[94mNone\u001b[0m:                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m in \u001b[92m<module>\u001b[0m:\u001b[94m13\u001b[0m                                                                                   \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.9/dist-packages/peft/\u001b[0m\u001b[1;33mpeft_model.py\u001b[0m:\u001b[94m721\u001b[0m in \u001b[92mgenerate\u001b[0m                        \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m 718 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[96mself\u001b[0m.base_model.prepare_inputs_for_generation = \u001b[96mself\u001b[0m.prepare_inputs_for_generati  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m 719 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mtry\u001b[0m:                                                                              \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m 720 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[94mif\u001b[0m \u001b[95mnot\u001b[0m \u001b[96misinstance\u001b[0m(peft_config, PromptLearningConfig):                         \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 721 \u001b[2m│   │   │   │   \u001b[0moutputs = \u001b[96mself\u001b[0m.base_model.generate(**kwargs)                              \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m 722 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[94melse\u001b[0m:                                                                         \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m 723 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m\u001b[94mif\u001b[0m \u001b[33m\"\u001b[0m\u001b[33minput_ids\u001b[0m\u001b[33m\"\u001b[0m \u001b[95mnot\u001b[0m \u001b[95min\u001b[0m kwargs:                                             \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m 724 \u001b[0m\u001b[2m│   │   │   │   │   \u001b[0m\u001b[94mraise\u001b[0m \u001b[96mValueError\u001b[0m(\u001b[33m\"\u001b[0m\u001b[33minput_ids must be provided for Peft model generati\u001b[0m  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.9/dist-packages/torch/utils/\u001b[0m\u001b[1;33m_contextlib.py\u001b[0m:\u001b[94m115\u001b[0m in \u001b[92mdecorate_context\u001b[0m        \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m112 \u001b[0m\u001b[2m│   \u001b[0m\u001b[1;95m@functools\u001b[0m.wraps(func)                                                                 \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m113 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mdef\u001b[0m \u001b[92mdecorate_context\u001b[0m(*args, **kwargs):                                                 \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m114 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mwith\u001b[0m ctx_factory():                                                                \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m115 \u001b[2m│   │   │   \u001b[0m\u001b[94mreturn\u001b[0m func(*args, **kwargs)                                                   \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m116 \u001b[0m\u001b[2m│   \u001b[0m                                                                                       \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m117 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mreturn\u001b[0m decorate_context                                                                \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m118 \u001b[0m                                                                                           \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.9/dist-packages/transformers/generation/\u001b[0m\u001b[1;33mutils.py\u001b[0m:\u001b[94m1406\u001b[0m in \u001b[92mgenerate\u001b[0m         \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1403 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m)                                                                         \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1404 \u001b[0m\u001b[2m│   │   │   \u001b[0m                                                                              \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1405 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[2m# 11. run greedy search\u001b[0m                                                       \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m1406 \u001b[2m│   │   │   \u001b[0m\u001b[94mreturn\u001b[0m \u001b[96mself\u001b[0m.greedy_search(                                                    \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1407 \u001b[0m\u001b[2m│   │   │   │   \u001b[0minput_ids,                                                                \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1408 \u001b[0m\u001b[2m│   │   │   │   \u001b[0mlogits_processor=logits_processor,                                        \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1409 \u001b[0m\u001b[2m│   │   │   │   \u001b[0mstopping_criteria=stopping_criteria,                                      \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.9/dist-packages/transformers/generation/\u001b[0m\u001b[1;33mutils.py\u001b[0m:\u001b[94m2201\u001b[0m in \u001b[92mgreedy_search\u001b[0m    \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m2198 \u001b[0m\u001b[2m│   │   │   \u001b[0mmodel_inputs = \u001b[96mself\u001b[0m.prepare_inputs_for_generation(input_ids, **model_kwargs)  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m2199 \u001b[0m\u001b[2m│   │   │   \u001b[0m                                                                              \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m2200 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[2m# forward pass to get next token\u001b[0m                                              \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m2201 \u001b[2m│   │   │   \u001b[0moutputs = \u001b[96mself\u001b[0m(                                                               \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m2202 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m**model_inputs,                                                           \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m2203 \u001b[0m\u001b[2m│   │   │   │   \u001b[0mreturn_dict=\u001b[94mTrue\u001b[0m,                                                         \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m2204 \u001b[0m\u001b[2m│   │   │   │   \u001b[0moutput_attentions=output_attentions,                                      \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.9/dist-packages/torch/nn/modules/\u001b[0m\u001b[1;33mmodule.py\u001b[0m:\u001b[94m1501\u001b[0m in \u001b[92m_call_impl\u001b[0m             \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1498 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mif\u001b[0m \u001b[95mnot\u001b[0m (\u001b[96mself\u001b[0m._backward_hooks \u001b[95mor\u001b[0m \u001b[96mself\u001b[0m._backward_pre_hooks \u001b[95mor\u001b[0m \u001b[96mself\u001b[0m._forward_hooks   \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1499 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m\u001b[95mor\u001b[0m _global_backward_pre_hooks \u001b[95mor\u001b[0m _global_backward_hooks                   \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1500 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m\u001b[95mor\u001b[0m _global_forward_hooks \u001b[95mor\u001b[0m _global_forward_pre_hooks):                   \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m1501 \u001b[2m│   │   │   \u001b[0m\u001b[94mreturn\u001b[0m forward_call(*args, **kwargs)                                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1502 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[2m# Do not call functions when jit is used\u001b[0m                                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1503 \u001b[0m\u001b[2m│   │   \u001b[0mfull_backward_hooks, non_full_backward_hooks = [], []                             \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1504 \u001b[0m\u001b[2m│   │   \u001b[0mbackward_pre_hooks = []                                                           \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.9/dist-packages/accelerate/\u001b[0m\u001b[1;33mhooks.py\u001b[0m:\u001b[94m165\u001b[0m in \u001b[92mnew_forward\u001b[0m                    \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m162 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[94mwith\u001b[0m torch.no_grad():                                                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m163 \u001b[0m\u001b[2m│   │   │   │   \u001b[0moutput = old_forward(*args, **kwargs)                                      \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m164 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94melse\u001b[0m:                                                                              \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m165 \u001b[2m│   │   │   \u001b[0moutput = old_forward(*args, **kwargs)                                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m166 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mreturn\u001b[0m module._hf_hook.post_forward(module, output)                                \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m167 \u001b[0m\u001b[2m│   \u001b[0m                                                                                       \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m168 \u001b[0m\u001b[2m│   \u001b[0mmodule.forward = new_forward                                                           \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.9/dist-packages/transformers/models/llama/\u001b[0m\u001b[1;33mmodeling_llama.py\u001b[0m:\u001b[94m770\u001b[0m in        \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[92mforward\u001b[0m                                                                                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m767 \u001b[0m\u001b[2m│   │   \u001b[0mreturn_dict = return_dict \u001b[94mif\u001b[0m return_dict \u001b[95mis\u001b[0m \u001b[95mnot\u001b[0m \u001b[94mNone\u001b[0m \u001b[94melse\u001b[0m \u001b[96mself\u001b[0m.config.use_return   \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m768 \u001b[0m\u001b[2m│   │   \u001b[0m                                                                                   \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m769 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[2m# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)\u001b[0m    \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m770 \u001b[2m│   │   \u001b[0moutputs = \u001b[96mself\u001b[0m.model(                                                              \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m771 \u001b[0m\u001b[2m│   │   │   \u001b[0minput_ids=input_ids,                                                           \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m772 \u001b[0m\u001b[2m│   │   │   \u001b[0mattention_mask=attention_mask,                                                 \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m773 \u001b[0m\u001b[2m│   │   │   \u001b[0mpast_key_values=past_key_values,                                               \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.9/dist-packages/torch/nn/modules/\u001b[0m\u001b[1;33mmodule.py\u001b[0m:\u001b[94m1501\u001b[0m in \u001b[92m_call_impl\u001b[0m             \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1498 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mif\u001b[0m \u001b[95mnot\u001b[0m (\u001b[96mself\u001b[0m._backward_hooks \u001b[95mor\u001b[0m \u001b[96mself\u001b[0m._backward_pre_hooks \u001b[95mor\u001b[0m \u001b[96mself\u001b[0m._forward_hooks   \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1499 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m\u001b[95mor\u001b[0m _global_backward_pre_hooks \u001b[95mor\u001b[0m _global_backward_hooks                   \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1500 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m\u001b[95mor\u001b[0m _global_forward_hooks \u001b[95mor\u001b[0m _global_forward_pre_hooks):                   \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m1501 \u001b[2m│   │   │   \u001b[0m\u001b[94mreturn\u001b[0m forward_call(*args, **kwargs)                                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1502 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[2m# Do not call functions when jit is used\u001b[0m                                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1503 \u001b[0m\u001b[2m│   │   \u001b[0mfull_backward_hooks, non_full_backward_hooks = [], []                             \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1504 \u001b[0m\u001b[2m│   │   \u001b[0mbackward_pre_hooks = []                                                           \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.9/dist-packages/accelerate/\u001b[0m\u001b[1;33mhooks.py\u001b[0m:\u001b[94m165\u001b[0m in \u001b[92mnew_forward\u001b[0m                    \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m162 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[94mwith\u001b[0m torch.no_grad():                                                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m163 \u001b[0m\u001b[2m│   │   │   │   \u001b[0moutput = old_forward(*args, **kwargs)                                      \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m164 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94melse\u001b[0m:                                                                              \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m165 \u001b[2m│   │   │   \u001b[0moutput = old_forward(*args, **kwargs)                                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m166 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mreturn\u001b[0m module._hf_hook.post_forward(module, output)                                \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m167 \u001b[0m\u001b[2m│   \u001b[0m                                                                                       \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m168 \u001b[0m\u001b[2m│   \u001b[0mmodule.forward = new_forward                                                           \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.9/dist-packages/transformers/models/llama/\u001b[0m\u001b[1;33mmodeling_llama.py\u001b[0m:\u001b[94m619\u001b[0m in        \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[92mforward\u001b[0m                                                                                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m616 \u001b[0m\u001b[2m│   │   │   │   │   \u001b[0m\u001b[94mNone\u001b[0m,                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m617 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m)                                                                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m618 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[94melse\u001b[0m:                                                                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m619 \u001b[2m│   │   │   │   \u001b[0mlayer_outputs = decoder_layer(                                             \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m620 \u001b[0m\u001b[2m│   │   │   │   │   \u001b[0mhidden_states,                                                         \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m621 \u001b[0m\u001b[2m│   │   │   │   │   \u001b[0mattention_mask=attention_mask,                                         \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m622 \u001b[0m\u001b[2m│   │   │   │   │   \u001b[0mpast_key_value=past_key_value,                                         \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.9/dist-packages/torch/nn/modules/\u001b[0m\u001b[1;33mmodule.py\u001b[0m:\u001b[94m1501\u001b[0m in \u001b[92m_call_impl\u001b[0m             \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1498 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mif\u001b[0m \u001b[95mnot\u001b[0m (\u001b[96mself\u001b[0m._backward_hooks \u001b[95mor\u001b[0m \u001b[96mself\u001b[0m._backward_pre_hooks \u001b[95mor\u001b[0m \u001b[96mself\u001b[0m._forward_hooks   \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1499 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m\u001b[95mor\u001b[0m _global_backward_pre_hooks \u001b[95mor\u001b[0m _global_backward_hooks                   \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1500 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m\u001b[95mor\u001b[0m _global_forward_hooks \u001b[95mor\u001b[0m _global_forward_pre_hooks):                   \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m1501 \u001b[2m│   │   │   \u001b[0m\u001b[94mreturn\u001b[0m forward_call(*args, **kwargs)                                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1502 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[2m# Do not call functions when jit is used\u001b[0m                                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1503 \u001b[0m\u001b[2m│   │   \u001b[0mfull_backward_hooks, non_full_backward_hooks = [], []                             \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1504 \u001b[0m\u001b[2m│   │   \u001b[0mbackward_pre_hooks = []                                                           \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.9/dist-packages/accelerate/\u001b[0m\u001b[1;33mhooks.py\u001b[0m:\u001b[94m165\u001b[0m in \u001b[92mnew_forward\u001b[0m                    \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m162 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[94mwith\u001b[0m torch.no_grad():                                                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m163 \u001b[0m\u001b[2m│   │   │   │   \u001b[0moutput = old_forward(*args, **kwargs)                                      \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m164 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94melse\u001b[0m:                                                                              \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m165 \u001b[2m│   │   │   \u001b[0moutput = old_forward(*args, **kwargs)                                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m166 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mreturn\u001b[0m module._hf_hook.post_forward(module, output)                                \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m167 \u001b[0m\u001b[2m│   \u001b[0m                                                                                       \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m168 \u001b[0m\u001b[2m│   \u001b[0mmodule.forward = new_forward                                                           \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.9/dist-packages/transformers/models/llama/\u001b[0m\u001b[1;33mmodeling_llama.py\u001b[0m:\u001b[94m316\u001b[0m in        \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[92mforward\u001b[0m                                                                                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m313 \u001b[0m\u001b[2m│   │   \u001b[0mhidden_states = \u001b[96mself\u001b[0m.input_layernorm(hidden_states)                                \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m314 \u001b[0m\u001b[2m│   │   \u001b[0m                                                                                   \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m315 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[2m# Self Attention\u001b[0m                                                                   \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m316 \u001b[2m│   │   \u001b[0mhidden_states, self_attn_weights, present_key_value = \u001b[96mself\u001b[0m.self_attn(              \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m317 \u001b[0m\u001b[2m│   │   │   \u001b[0mhidden_states=hidden_states,                                                   \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m318 \u001b[0m\u001b[2m│   │   │   \u001b[0mpast_key_value=past_key_value,                                                 \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m319 \u001b[0m\u001b[2m│   │   │   \u001b[0mattention_mask=attention_mask,                                                 \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.9/dist-packages/torch/nn/modules/\u001b[0m\u001b[1;33mmodule.py\u001b[0m:\u001b[94m1501\u001b[0m in \u001b[92m_call_impl\u001b[0m             \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1498 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mif\u001b[0m \u001b[95mnot\u001b[0m (\u001b[96mself\u001b[0m._backward_hooks \u001b[95mor\u001b[0m \u001b[96mself\u001b[0m._backward_pre_hooks \u001b[95mor\u001b[0m \u001b[96mself\u001b[0m._forward_hooks   \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1499 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m\u001b[95mor\u001b[0m _global_backward_pre_hooks \u001b[95mor\u001b[0m _global_backward_hooks                   \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1500 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m\u001b[95mor\u001b[0m _global_forward_hooks \u001b[95mor\u001b[0m _global_forward_pre_hooks):                   \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m1501 \u001b[2m│   │   │   \u001b[0m\u001b[94mreturn\u001b[0m forward_call(*args, **kwargs)                                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1502 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[2m# Do not call functions when jit is used\u001b[0m                                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1503 \u001b[0m\u001b[2m│   │   \u001b[0mfull_backward_hooks, non_full_backward_hooks = [], []                             \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1504 \u001b[0m\u001b[2m│   │   \u001b[0mbackward_pre_hooks = []                                                           \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.9/dist-packages/accelerate/\u001b[0m\u001b[1;33mhooks.py\u001b[0m:\u001b[94m165\u001b[0m in \u001b[92mnew_forward\u001b[0m                    \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m162 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[94mwith\u001b[0m torch.no_grad():                                                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m163 \u001b[0m\u001b[2m│   │   │   │   \u001b[0moutput = old_forward(*args, **kwargs)                                      \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m164 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94melse\u001b[0m:                                                                              \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m165 \u001b[2m│   │   │   \u001b[0moutput = old_forward(*args, **kwargs)                                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m166 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mreturn\u001b[0m module._hf_hook.post_forward(module, output)                                \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m167 \u001b[0m\u001b[2m│   \u001b[0m                                                                                       \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m168 \u001b[0m\u001b[2m│   \u001b[0mmodule.forward = new_forward                                                           \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.9/dist-packages/transformers/models/llama/\u001b[0m\u001b[1;33mmodeling_llama.py\u001b[0m:\u001b[94m216\u001b[0m in        \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[92mforward\u001b[0m                                                                                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m213 \u001b[0m\u001b[2m│   │   \u001b[0m                                                                                   \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m214 \u001b[0m\u001b[2m│   │   \u001b[0mbsz, q_len, _ = hidden_states.size()                                               \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m215 \u001b[0m\u001b[2m│   │   \u001b[0m                                                                                   \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m216 \u001b[2m│   │   \u001b[0mquery_states = \u001b[96mself\u001b[0m.q_proj(hidden_states).view(bsz, q_len, \u001b[96mself\u001b[0m.num_heads, \u001b[96mself\u001b[0m.   \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m217 \u001b[0m\u001b[2m│   │   \u001b[0mkey_states = \u001b[96mself\u001b[0m.k_proj(hidden_states).view(bsz, q_len, \u001b[96mself\u001b[0m.num_heads, \u001b[96mself\u001b[0m.he   \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m218 \u001b[0m\u001b[2m│   │   \u001b[0mvalue_states = \u001b[96mself\u001b[0m.v_proj(hidden_states).view(bsz, q_len, \u001b[96mself\u001b[0m.num_heads, \u001b[96mself\u001b[0m.   \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m219 \u001b[0m                                                                                           \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.9/dist-packages/torch/nn/modules/\u001b[0m\u001b[1;33mmodule.py\u001b[0m:\u001b[94m1501\u001b[0m in \u001b[92m_call_impl\u001b[0m             \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1498 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mif\u001b[0m \u001b[95mnot\u001b[0m (\u001b[96mself\u001b[0m._backward_hooks \u001b[95mor\u001b[0m \u001b[96mself\u001b[0m._backward_pre_hooks \u001b[95mor\u001b[0m \u001b[96mself\u001b[0m._forward_hooks   \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1499 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m\u001b[95mor\u001b[0m _global_backward_pre_hooks \u001b[95mor\u001b[0m _global_backward_hooks                   \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1500 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m\u001b[95mor\u001b[0m _global_forward_hooks \u001b[95mor\u001b[0m _global_forward_pre_hooks):                   \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m1501 \u001b[2m│   │   │   \u001b[0m\u001b[94mreturn\u001b[0m forward_call(*args, **kwargs)                                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1502 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[2m# Do not call functions when jit is used\u001b[0m                                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1503 \u001b[0m\u001b[2m│   │   \u001b[0mfull_backward_hooks, non_full_backward_hooks = [], []                             \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1504 \u001b[0m\u001b[2m│   │   \u001b[0mbackward_pre_hooks = []                                                           \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.9/dist-packages/peft/tuners/\u001b[0m\u001b[1;33mlora.py\u001b[0m:\u001b[94m557\u001b[0m in \u001b[92mforward\u001b[0m                        \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m554 \u001b[0m\u001b[2m│   │   │   │   │   │   \u001b[0mx = x.float()                                                      \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m555 \u001b[0m\u001b[2m│   │   │   │   │   \u001b[0moutput = (                                                             \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m556 \u001b[0m\u001b[2m│   │   │   │   │   │   \u001b[0m\u001b[96mself\u001b[0m.lora_B[\u001b[96mself\u001b[0m.active_adapter](                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m557 \u001b[2m│   │   │   │   │   │   │   \u001b[0m\u001b[96mself\u001b[0m.lora_A[\u001b[96mself\u001b[0m.active_adapter](\u001b[96mself\u001b[0m.lora_dropout[\u001b[96mself\u001b[0m.acti   \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m558 \u001b[0m\u001b[2m│   │   │   │   │   │   \u001b[0m).to(expected_dtype)                                               \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m559 \u001b[0m\u001b[2m│   │   │   │   │   │   \u001b[0m* \u001b[96mself\u001b[0m.scaling[\u001b[96mself\u001b[0m.active_adapter]                                \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m560 \u001b[0m\u001b[2m│   │   │   │   │   \u001b[0m)                                                                      \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.9/dist-packages/torch/nn/modules/\u001b[0m\u001b[1;33mmodule.py\u001b[0m:\u001b[94m1501\u001b[0m in \u001b[92m_call_impl\u001b[0m             \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1498 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mif\u001b[0m \u001b[95mnot\u001b[0m (\u001b[96mself\u001b[0m._backward_hooks \u001b[95mor\u001b[0m \u001b[96mself\u001b[0m._backward_pre_hooks \u001b[95mor\u001b[0m \u001b[96mself\u001b[0m._forward_hooks   \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1499 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m\u001b[95mor\u001b[0m _global_backward_pre_hooks \u001b[95mor\u001b[0m _global_backward_hooks                   \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1500 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m\u001b[95mor\u001b[0m _global_forward_hooks \u001b[95mor\u001b[0m _global_forward_pre_hooks):                   \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m1501 \u001b[2m│   │   │   \u001b[0m\u001b[94mreturn\u001b[0m forward_call(*args, **kwargs)                                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1502 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[2m# Do not call functions when jit is used\u001b[0m                                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1503 \u001b[0m\u001b[2m│   │   \u001b[0mfull_backward_hooks, non_full_backward_hooks = [], []                             \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1504 \u001b[0m\u001b[2m│   │   \u001b[0mbackward_pre_hooks = []                                                           \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.9/dist-packages/torch/nn/modules/\u001b[0m\u001b[1;33mlinear.py\u001b[0m:\u001b[94m114\u001b[0m in \u001b[92mforward\u001b[0m                 \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m111 \u001b[0m\u001b[2m│   │   │   \u001b[0minit.uniform_(\u001b[96mself\u001b[0m.bias, -bound, bound)                                        \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m112 \u001b[0m\u001b[2m│   \u001b[0m                                                                                       \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m113 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mdef\u001b[0m \u001b[92mforward\u001b[0m(\u001b[96mself\u001b[0m, \u001b[96minput\u001b[0m: Tensor) -> Tensor:                                            \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m114 \u001b[2m│   │   \u001b[0m\u001b[94mreturn\u001b[0m F.linear(\u001b[96minput\u001b[0m, \u001b[96mself\u001b[0m.weight, \u001b[96mself\u001b[0m.bias)                                     \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m115 \u001b[0m\u001b[2m│   \u001b[0m                                                                                       \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m116 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mdef\u001b[0m \u001b[92mextra_repr\u001b[0m(\u001b[96mself\u001b[0m) -> \u001b[96mstr\u001b[0m:                                                           \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m117 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mreturn\u001b[0m \u001b[33m'\u001b[0m\u001b[33min_features=\u001b[0m\u001b[33m{}\u001b[0m\u001b[33m, out_features=\u001b[0m\u001b[33m{}\u001b[0m\u001b[33m, bias=\u001b[0m\u001b[33m{}\u001b[0m\u001b[33m'\u001b[0m.format(                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n",
       "\u001b[1;91mRuntimeError: \u001b[0mexpected scalar type Float but found Half\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "inputs = tokenizer(\n",
    "    PROMPT,\n",
    "    return_tensors=\"pt\",\n",
    ")\n",
    "input_ids = inputs[\"input_ids\"].cuda()\n",
    "\n",
    "generation_config = GenerationConfig(\n",
    "    temperature=0.6,\n",
    "    top_p=0.95,\n",
    "    repetition_penalty=1.15,\n",
    ")\n",
    "print(\"Generating...\")\n",
    "generation_output = model.generate(\n",
    "    input_ids=input_ids,\n",
    "    generation_config=generation_config,\n",
    "    return_dict_in_generate=True,\n",
    "    output_scores=True,\n",
    "    max_new_tokens=128,\n",
    ")\n",
    "for s in generation_output.sequences:\n",
    "    print(tokenizer.decode(s))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "iDIUL7sje52h"
   },
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "\n",
    "def is_prime(number):  # Checks if a given integer is a prime number.\n",
    "    for i in range (2, int(math.ceil(math.sqrt(number)))+1):\n",
    "        if number % i == 0:\n",
    "            return False\n",
    "    return True"
   ]
  },
  {
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   "execution_count": null,
   "metadata": {
    "colab": {
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    },
    "id": "eVZP2Xf8rWJe",
    "outputId": "527a31b3-d050-4e49-c546-e5a01ea10b39"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is_prime(23)"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "hPtf750brYSg"
   },
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   "source": []
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