OPT-IML-30B.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "870e8279",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-02-05 15:15:51.727901: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import json\n",
    "\n",
    "import torch\n",
    "import tqdm\n",
    "\n",
    "from transformers import AutoModelForCausalLM, AutoTokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "677130fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = json.load(open(\"Complete_dataset/test.json\"))\n",
    "files = os.listdir(\"Complete_dataset/CT json/\")\n",
    "files.remove(\".DS_Store\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d5df4e00",
   "metadata": {},
   "outputs": [],
   "source": [
    "files_data = {file[:-5]:json.load(open(f\"Complete_dataset/CT json/{file}\")) for file in files}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "07636837",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_expanded = []\n",
    "for _id, value in data.items():\n",
    "    temp = {}\n",
    "    temp[\"id\"] = _id\n",
    "    p_nctid = value[\"Primary_id\"]\n",
    "    s_nctid = value.get(\"Secondary_id\")\n",
    "    section_id = value[\"Section_id\"]\n",
    "    statement = value[\"Statement\"]\n",
    "    primary_evidence = files_data[p_nctid][section_id]\n",
    "    temp[\"statement\"] = statement\n",
    "    temp[\"primary_evidence\"] = primary_evidence\n",
    "#     temp[\"label\"] = value[\"Label\"]\n",
    "    \n",
    "    if s_nctid is not None:\n",
    "        secondary_evidence = files_data[s_nctid][section_id]\n",
    "        temp[\"secondary_evidence\"] = secondary_evidence\n",
    "    \n",
    "    data_expanded.append(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e0a6c139",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(\"facebook/opt-iml-max-30b\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dce56a86",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = AutoModelForCausalLM.from_pretrained(\"facebook/opt-iml-max-30b\", torch_dtype=torch.float16).cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab1349c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_input_text(premise, hypothesis):\n",
    "    options_prefix = \"OPTIONS:\\n- \"\n",
    "    separator = \"\\n- \"\n",
    "    options_ = options_prefix + f\"{separator}\".join([\"Entailment\",\"Contradiction\"])\n",
    "    return f\"{premise} \\n Question: Does this imply that {hypothesis}? {options_}\\n A:\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a307dd6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "samples = []\n",
    "for sample in data_expanded:\n",
    "    primary_evidence = \"\".join(sample['primary_evidence'])\n",
    "    sentence = f\"Primary trial evidence are {primary_evidence}\"\n",
    "    secondary_evidence = sample.get(\"secondary_evidence\")\n",
    "    if secondary_evidence:\n",
    "        secondary_evidence = \"\".join(sample['secondary_evidence'])\n",
    "        sentence = f\"{sentence} Secondary trial evidence are {secondary_evidence}\"\n",
    "    input_text = get_input_text(sentence, sample['statement'])\n",
    "    temp = {\"text\":input_text, \"label\":0}\n",
    "    samples.append(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5d62610",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/500 [00:00<?, ?it/s]/home/ubuntu/transformers/src/transformers/generation/utils.py:1273: UserWarning: Neither `max_length` nor `max_new_tokens` has been set, `max_length` will default to 20 (`generation_config.max_length`). Controlling `max_length` via the config is deprecated and `max_length` will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.\n",
      "  warnings.warn(\n",
      "Input length of input_ids is 269, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "  0%|          | 1/500 [00:06<50:03,  6.02s/it]Input length of input_ids is 138, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "Input length of input_ids is 178, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "  1%|          | 3/500 [00:06<13:27,  1.62s/it]Input length of input_ids is 681, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "  1%|          | 4/500 [00:06<09:32,  1.15s/it]Input length of input_ids is 456, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "  1%|          | 5/500 [00:06<06:50,  1.21it/s]Input length of input_ids is 110, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "Input length of input_ids is 1139, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "  1%|▏         | 7/500 [00:07<04:31,  1.82it/s]Input length of input_ids is 1062, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "  2%|▏         | 8/500 [00:07<04:10,  1.96it/s]Input length of input_ids is 121, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "Input length of input_ids is 311, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "  2%|▏         | 10/500 [00:07<02:45,  2.96it/s]Input length of input_ids is 904, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "  2%|▏         | 11/500 [00:08<02:42,  3.00it/s]Input length of input_ids is 938, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "  2%|▏         | 12/500 [00:08<02:42,  3.01it/s]Input length of input_ids is 230, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "  3%|▎         | 13/500 [00:08<02:12,  3.68it/s]Input length of input_ids is 176, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "Input length of input_ids is 878, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "  3%|▎         | 15/500 [00:08<01:55,  4.21it/s]Input length of input_ids is 1035, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "  3%|▎         | 16/500 [00:09<02:14,  3.61it/s]Input length of input_ids is 250, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "  3%|▎         | 17/500 [00:09<01:52,  4.28it/s]Input length of input_ids is 680, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "  4%|▎         | 18/500 [00:09<01:53,  4.24it/s]Input length of input_ids is 415, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "  4%|▍         | 19/500 [00:09<01:45,  4.57it/s]Input length of input_ids is 367, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "  4%|▍         | 20/500 [00:09<01:32,  5.17it/s]Input length of input_ids is 343, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "  4%|▍         | 21/500 [00:10<01:24,  5.70it/s]Input length of input_ids is 130, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "Input length of input_ids is 597, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "  5%|▍         | 23/500 [00:10<01:18,  6.06it/s]Input length of input_ids is 73, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "  5%|▍         | 24/500 [00:10<01:19,  5.99it/s]Input length of input_ids is 926, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "  5%|▌         | 25/500 [00:10<01:39,  4.80it/s]Input length of input_ids is 767, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "  5%|▌         | 26/500 [00:11<01:47,  4.40it/s]Input length of input_ids is 210, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "  5%|▌         | 27/500 [00:11<01:30,  5.20it/s]Input length of input_ids is 161, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "Input length of input_ids is 537, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "  6%|▌         | 29/500 [00:11<01:20,  5.83it/s]Input length of input_ids is 359, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "  6%|▌         | 30/500 [00:11<01:16,  6.15it/s]Input length of input_ids is 1274, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "  6%|▌         | 31/500 [00:12<01:51,  4.20it/s]Input length of input_ids is 401, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "  6%|▋         | 32/500 [00:12<01:43,  4.54it/s]Input length of input_ids is 319, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "  7%|▋         | 33/500 [00:12<01:31,  5.12it/s]Input length of input_ids is 206, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "Input length of input_ids is 900, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "  7%|▋         | 35/500 [00:12<01:34,  4.95it/s]Input length of input_ids is 548, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "  7%|▋         | 36/500 [00:13<01:33,  4.94it/s]Input length of input_ids is 712, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "  7%|▋         | 37/500 [00:13<01:38,  4.69it/s]Input length of input_ids is 175, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "Input length of input_ids is 1651, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "  8%|▊         | 39/500 [00:14<02:10,  3.54it/s]Input length of input_ids is 409, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "  8%|▊         | 40/500 [00:14<01:58,  3.89it/s]Input length of input_ids is 273, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "  8%|▊         | 41/500 [00:14<01:43,  4.45it/s]Input length of input_ids is 1011, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "  8%|▊         | 42/500 [00:14<01:59,  3.84it/s]Input length of input_ids is 545, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "  9%|▊         | 43/500 [00:14<01:52,  4.06it/s]Input length of input_ids is 676, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "  9%|▉         | 44/500 [00:15<01:51,  4.08it/s]Input length of input_ids is 452, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "  9%|▉         | 45/500 [00:15<01:41,  4.47it/s]Input length of input_ids is 423, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "  9%|▉         | 46/500 [00:15<01:35,  4.78it/s]Input length of input_ids is 135, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "Input length of input_ids is 432, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      " 10%|▉         | 48/500 [00:15<01:18,  5.77it/s]Input length of input_ids is 457, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
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      "Input length of input_ids is 322, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      " 10%|█         | 51/500 [00:16<01:04,  6.94it/s]Input length of input_ids is 416, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      " 10%|█         | 52/500 [00:16<01:06,  6.71it/s]Input length of input_ids is 599, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      " 11%|█         | 53/500 [00:16<01:13,  6.07it/s]Input length of input_ids is 172, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "Input length of input_ids is 510, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      " 11%|█         | 55/500 [00:16<01:06,  6.66it/s]Input length of input_ids is 699, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
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      "Input length of input_ids is 347, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
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       "<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;module&gt;</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\">4 │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">for</span> sample <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">in</span> tqdm.tqdm(samples):                                                        <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\">5 │   │   </span>labels.append(sample[<span style=\"color: #808000; text-decoration-color: #808000\">\"label\"</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\">6 │   │   </span>input_ids = tokenizer(sample[<span style=\"color: #808000; text-decoration-color: #808000\">\"text\"</span>], return_tensors=<span style=\"color: #808000; text-decoration-color: #808000\">\"pt\"</span>,).input_ids.to(<span style=\"color: #808000; text-decoration-color: #808000\">\"cuda\"</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>7 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>outputs = model.generate(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\">8 │   │   </span>pred.append(tokenizer.decode(outputs[<span style=\"color: #0000ff; text-decoration-color: #0000ff\">0</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\">9 </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\">/home/ubuntu/env/lib/python3.8/site-packages/torch/autograd/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">grad_mode.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">27</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\"> 24 │   │   </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\"> 25 │   │   </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\"> 26 │   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">with</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.clone():                                                             <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> 27 <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\"> 28 │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> cast(F, 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\"> 29 │   </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\"> 30 │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">def</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">_wrap_generator</span>(<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>, func):                                                       <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\">/home/ubuntu/transformers/src/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\">1391</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\">1388 │   │   │   │   </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\">1389 │   │   │   </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\">1390 │   │   │   # 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>1391 <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\">1392 │   │   │   │   </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\">1393 │   │   │   │   </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\">1394 │   │   │   │   </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\">/home/ubuntu/transformers/src/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\">2179</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\">2176 │   │   │   </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\">2177 │   │   │   </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\">2178 │   │   │   # 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>2179 <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\">2180 │   │   │   │   </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\">2181 │   │   │   │   </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\">2182 │   │   │   │   </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\">/home/ubuntu/env/lib/python3.8/site-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\">1194</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\">1191 │   │   # this function, and just call forward.</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\">1192 │   │   </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>._forward_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._forward_pre_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">o</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\">1193 │   │   │   │   </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>1194 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> forward_call(*<span style=\"color: #00ffff; text-decoration-color: #00ffff\">input</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\">1195 │   │   # 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\">1196 │   │   </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\">1197 │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._backward_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: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/home/ubuntu/env/lib/python3.8/site-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\">156</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\">153 │   │   │   </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\">154 │   │   │   │   </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\">155 │   │   </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>156 <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\">157 │   │   </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\">158 │   </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\">159 │   </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\">/home/ubuntu/transformers/src/transformers/models/opt/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">modeling_opt.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">934</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\"> 931 │   │   </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\"> 932 │   │   </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\"> 933 │   │   # 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> 934 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>outputs = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.model.decoder(                                                     <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\"> 935 │   │   │   </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\"> 936 │   │   │   </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\"> 937 │   │   │   </span>head_mask=head_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\">/home/ubuntu/env/lib/python3.8/site-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\">1194</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\">1191 │   │   # this function, and just call forward.</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\">1192 │   │   </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>._forward_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._forward_pre_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">o</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\">1193 │   │   │   │   </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>1194 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> forward_call(*<span style=\"color: #00ffff; text-decoration-color: #00ffff\">input</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\">1195 │   │   # 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\">1196 │   │   </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\">1197 │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._backward_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: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/home/ubuntu/transformers/src/transformers/models/opt/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">modeling_opt.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">698</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\"> 695 │   │   │   │   </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\"> 696 │   │   │   </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\"> 697 │   │   │   │   </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> 698 <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\"> 699 │   │   │   │   │   </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\"> 700 │   │   │   │   │   </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\"> 701 │   │   │   │   │   </span>layer_head_mask=(head_mask[idx] <span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> head_mask <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: #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: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/home/ubuntu/env/lib/python3.8/site-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\">1194</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\">1191 │   │   # this function, and just call forward.</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\">1192 │   │   </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>._forward_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._forward_pre_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">o</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\">1193 │   │   │   │   </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>1194 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> forward_call(*<span style=\"color: #00ffff; text-decoration-color: #00ffff\">input</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\">1195 │   │   # 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\">1196 │   │   </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\">1197 │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._backward_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: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/home/ubuntu/env/lib/python3.8/site-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\">156</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\">153 │   │   │   </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\">154 │   │   │   │   </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\">155 │   │   </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>156 <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\">157 │   │   </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\">158 │   </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\">159 │   </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\">/home/ubuntu/transformers/src/transformers/models/opt/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">modeling_opt.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">327</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\"> 324 │   │   │   </span>hidden_states = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.self_attn_layer_norm(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\"> 325 │   │   </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\"> 326 │   │   # 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> 327 <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\"> 328 │   │   │   </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\"> 329 │   │   │   </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\"> 330 │   │   │   </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\">/home/ubuntu/env/lib/python3.8/site-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\">1194</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\">1191 │   │   # this function, and just call forward.</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\">1192 │   │   </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>._forward_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._forward_pre_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">o</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\">1193 │   │   │   │   </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>1194 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> forward_call(*<span style=\"color: #00ffff; text-decoration-color: #00ffff\">input</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\">1195 │   │   # 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\">1196 │   │   </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\">1197 │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._backward_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: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/home/ubuntu/env/lib/python3.8/site-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\">156</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\">153 │   │   │   </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\">154 │   │   │   │   </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\">155 │   │   </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>156 <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\">157 │   │   </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\">158 │   </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\">159 │   </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\">/home/ubuntu/transformers/src/transformers/models/opt/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">modeling_opt.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">172</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\"> 169 │   │   </span>bsz, tgt_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\"> 170 │   │   </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\"> 171 │   │   # get query proj</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> 172 <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) * <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.scaling                          <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\"> 173 │   │   # get key, value proj</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\"> 174 │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> is_cross_attention <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">and</span> past_key_value <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>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 175 │   │   │   # reuse k,v, cross_attentions</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\">/home/ubuntu/env/lib/python3.8/site-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\">1194</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\">1191 │   │   # this function, and just call forward.</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\">1192 │   │   </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>._forward_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._forward_pre_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">o</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\">1193 │   │   │   │   </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>1194 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> forward_call(*<span style=\"color: #00ffff; text-decoration-color: #00ffff\">input</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\">1195 │   │   # 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\">1196 │   │   </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\">1197 │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._backward_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: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/home/ubuntu/env/lib/python3.8/site-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\">156</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\">153 │   │   │   </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\">154 │   │   │   │   </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\">155 │   │   </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>156 <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\">157 │   │   </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\">158 │   </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\">159 │   </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\">/home/ubuntu/env/lib/python3.8/site-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>CUDA error: CUBLAS_STATUS_EXECUTION_FAILED when calling `<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">cublasLtMatmul</span><span style=\"font-weight: bold\">(</span> ltHandle, \n",
       "<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">computeDesc.descriptor</span><span style=\"font-weight: bold\">()</span>, &amp;alpha_val, mat1_ptr, <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">Adesc.descriptor</span><span style=\"font-weight: bold\">()</span>, mat2_ptr, <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">Bdesc.descriptor</span><span style=\"font-weight: bold\">()</span>, &amp;beta_val, \n",
       "result_ptr, <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">Cdesc.descriptor</span><span style=\"font-weight: bold\">()</span>, result_ptr, <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">Cdesc.descriptor</span><span style=\"font-weight: bold\">()</span>, &amp;heuristicResult.algo, <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">workspace.data_ptr</span><span style=\"font-weight: bold\">()</span>, \n",
       "workspaceSize, at::cuda::<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">getCurrentCUDAStream</span><span style=\"font-weight: bold\">())</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<module>\u001b[0m                                                                                      \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m4 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mfor\u001b[0m sample \u001b[95min\u001b[0m tqdm.tqdm(samples):                                                        \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m5 \u001b[0m\u001b[2m│   │   \u001b[0mlabels.append(sample[\u001b[33m\"\u001b[0m\u001b[33mlabel\u001b[0m\u001b[33m\"\u001b[0m])                                                       \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m6 \u001b[0m\u001b[2m│   │   \u001b[0minput_ids = tokenizer(sample[\u001b[33m\"\u001b[0m\u001b[33mtext\u001b[0m\u001b[33m\"\u001b[0m], return_tensors=\u001b[33m\"\u001b[0m\u001b[33mpt\u001b[0m\u001b[33m\"\u001b[0m,).input_ids.to(\u001b[33m\"\u001b[0m\u001b[33mcuda\u001b[0m\u001b[33m\"\u001b[0m)     \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m7 \u001b[2m│   │   \u001b[0moutputs = model.generate(input_ids)                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m8 \u001b[0m\u001b[2m│   │   \u001b[0mpred.append(tokenizer.decode(outputs[\u001b[94m0\u001b[0m]))                                            \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m9 \u001b[0m                                                                                             \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[2;33m/home/ubuntu/env/lib/python3.8/site-packages/torch/autograd/\u001b[0m\u001b[1;33mgrad_mode.py\u001b[0m:\u001b[94m27\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[2m 24 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[1;95m@functools\u001b[0m.wraps(func)                                                             \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m 25 \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[2m 26 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[94mwith\u001b[0m \u001b[96mself\u001b[0m.clone():                                                             \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 27 \u001b[2m│   │   │   │   \u001b[0m\u001b[94mreturn\u001b[0m func(*args, **kwargs)                                               \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m 28 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mreturn\u001b[0m cast(F, decorate_context)                                                   \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m 29 \u001b[0m\u001b[2m│   \u001b[0m                                                                                       \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m 30 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mdef\u001b[0m \u001b[92m_wrap_generator\u001b[0m(\u001b[96mself\u001b[0m, func):                                                       \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[2;33m/home/ubuntu/transformers/src/transformers/generation/\u001b[0m\u001b[1;33mutils.py\u001b[0m:\u001b[94m1391\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[2m1388 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m)                                                                         \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1389 \u001b[0m\u001b[2m│   │   │   \u001b[0m                                                                              \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1390 \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[0m1391 \u001b[2m│   │   │   \u001b[0m\u001b[94mreturn\u001b[0m \u001b[96mself\u001b[0m.greedy_search(                                                    \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1392 \u001b[0m\u001b[2m│   │   │   │   \u001b[0minput_ids,                                                                \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1393 \u001b[0m\u001b[2m│   │   │   │   \u001b[0mlogits_processor=logits_processor,                                        \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1394 \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/home/ubuntu/transformers/src/transformers/generation/\u001b[0m\u001b[1;33mutils.py\u001b[0m:\u001b[94m2179\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[2m2176 \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[2m2177 \u001b[0m\u001b[2m│   │   │   \u001b[0m                                                                              \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m2178 \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[0m2179 \u001b[2m│   │   │   \u001b[0moutputs = \u001b[96mself\u001b[0m(                                                               \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m2180 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m**model_inputs,                                                           \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m2181 \u001b[0m\u001b[2m│   │   │   │   \u001b[0mreturn_dict=\u001b[94mTrue\u001b[0m,                                                         \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m2182 \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/home/ubuntu/env/lib/python3.8/site-packages/torch/nn/modules/\u001b[0m\u001b[1;33mmodule.py\u001b[0m:\u001b[94m1194\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[2m1191 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[2m# this function, and just call forward.\u001b[0m                                           \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1192 \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._forward_hooks \u001b[95mor\u001b[0m \u001b[96mself\u001b[0m._forward_pre_hooks \u001b[95mo\u001b[0m  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1193 \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[0m1194 \u001b[2m│   │   │   \u001b[0m\u001b[94mreturn\u001b[0m forward_call(*\u001b[96minput\u001b[0m, **kwargs)                                         \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1195 \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[2m1196 \u001b[0m\u001b[2m│   │   \u001b[0mfull_backward_hooks, non_full_backward_hooks = [], []                             \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1197 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mif\u001b[0m \u001b[96mself\u001b[0m._backward_hooks \u001b[95mor\u001b[0m _global_backward_hooks:                                \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[2;33m/home/ubuntu/env/lib/python3.8/site-packages/accelerate/\u001b[0m\u001b[1;33mhooks.py\u001b[0m:\u001b[94m156\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[2m153 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[94mwith\u001b[0m torch.no_grad():                                                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m154 \u001b[0m\u001b[2m│   │   │   │   \u001b[0moutput = old_forward(*args, **kwargs)                                      \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m155 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94melse\u001b[0m:                                                                              \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m156 \u001b[2m│   │   │   \u001b[0moutput = old_forward(*args, **kwargs)                                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m157 \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[2m158 \u001b[0m\u001b[2m│   \u001b[0m                                                                                       \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m159 \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/home/ubuntu/transformers/src/transformers/models/opt/\u001b[0m\u001b[1;33mmodeling_opt.py\u001b[0m:\u001b[94m934\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[2m 931 \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[2m 932 \u001b[0m\u001b[2m│   │   \u001b[0m                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m 933 \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[0m 934 \u001b[2m│   │   \u001b[0moutputs = \u001b[96mself\u001b[0m.model.decoder(                                                     \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m 935 \u001b[0m\u001b[2m│   │   │   \u001b[0minput_ids=input_ids,                                                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m 936 \u001b[0m\u001b[2m│   │   │   \u001b[0mattention_mask=attention_mask,                                                \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m 937 \u001b[0m\u001b[2m│   │   │   \u001b[0mhead_mask=head_mask,                                                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[2;33m/home/ubuntu/env/lib/python3.8/site-packages/torch/nn/modules/\u001b[0m\u001b[1;33mmodule.py\u001b[0m:\u001b[94m1194\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[2m1191 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[2m# this function, and just call forward.\u001b[0m                                           \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1192 \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._forward_hooks \u001b[95mor\u001b[0m \u001b[96mself\u001b[0m._forward_pre_hooks \u001b[95mo\u001b[0m  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1193 \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[0m1194 \u001b[2m│   │   │   \u001b[0m\u001b[94mreturn\u001b[0m forward_call(*\u001b[96minput\u001b[0m, **kwargs)                                         \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1195 \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[2m1196 \u001b[0m\u001b[2m│   │   \u001b[0mfull_backward_hooks, non_full_backward_hooks = [], []                             \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1197 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mif\u001b[0m \u001b[96mself\u001b[0m._backward_hooks \u001b[95mor\u001b[0m _global_backward_hooks:                                \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[2;33m/home/ubuntu/transformers/src/transformers/models/opt/\u001b[0m\u001b[1;33mmodeling_opt.py\u001b[0m:\u001b[94m698\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[2m 695 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m)                                                                         \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m 696 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[94melse\u001b[0m:                                                                         \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m 697 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m                                                                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 698 \u001b[2m│   │   │   │   \u001b[0mlayer_outputs = decoder_layer(                                            \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m 699 \u001b[0m\u001b[2m│   │   │   │   │   \u001b[0mhidden_states,                                                        \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m 700 \u001b[0m\u001b[2m│   │   │   │   │   \u001b[0mattention_mask=attention_mask,                                        \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m 701 \u001b[0m\u001b[2m│   │   │   │   │   \u001b[0mlayer_head_mask=(head_mask[idx] \u001b[94mif\u001b[0m head_mask \u001b[95mis\u001b[0m \u001b[95mnot\u001b[0m \u001b[94mNone\u001b[0m \u001b[94melse\u001b[0m \u001b[94mNone\u001b[0m),  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[2;33m/home/ubuntu/env/lib/python3.8/site-packages/torch/nn/modules/\u001b[0m\u001b[1;33mmodule.py\u001b[0m:\u001b[94m1194\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[2m1191 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[2m# this function, and just call forward.\u001b[0m                                           \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1192 \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._forward_hooks \u001b[95mor\u001b[0m \u001b[96mself\u001b[0m._forward_pre_hooks \u001b[95mo\u001b[0m  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1193 \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[0m1194 \u001b[2m│   │   │   \u001b[0m\u001b[94mreturn\u001b[0m forward_call(*\u001b[96minput\u001b[0m, **kwargs)                                         \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1195 \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[2m1196 \u001b[0m\u001b[2m│   │   \u001b[0mfull_backward_hooks, non_full_backward_hooks = [], []                             \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1197 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mif\u001b[0m \u001b[96mself\u001b[0m._backward_hooks \u001b[95mor\u001b[0m _global_backward_hooks:                                \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[2;33m/home/ubuntu/env/lib/python3.8/site-packages/accelerate/\u001b[0m\u001b[1;33mhooks.py\u001b[0m:\u001b[94m156\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[2m153 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[94mwith\u001b[0m torch.no_grad():                                                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m154 \u001b[0m\u001b[2m│   │   │   │   \u001b[0moutput = old_forward(*args, **kwargs)                                      \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m155 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94melse\u001b[0m:                                                                              \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m156 \u001b[2m│   │   │   \u001b[0moutput = old_forward(*args, **kwargs)                                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m157 \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[2m158 \u001b[0m\u001b[2m│   \u001b[0m                                                                                       \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m159 \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/home/ubuntu/transformers/src/transformers/models/opt/\u001b[0m\u001b[1;33mmodeling_opt.py\u001b[0m:\u001b[94m327\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[2m 324 \u001b[0m\u001b[2m│   │   │   \u001b[0mhidden_states = \u001b[96mself\u001b[0m.self_attn_layer_norm(hidden_states)                      \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m 325 \u001b[0m\u001b[2m│   │   \u001b[0m                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m 326 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[2m# Self Attention\u001b[0m                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 327 \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[2m 328 \u001b[0m\u001b[2m│   │   │   \u001b[0mhidden_states=hidden_states,                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m 329 \u001b[0m\u001b[2m│   │   │   \u001b[0mpast_key_value=past_key_value,                                                \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m 330 \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/home/ubuntu/env/lib/python3.8/site-packages/torch/nn/modules/\u001b[0m\u001b[1;33mmodule.py\u001b[0m:\u001b[94m1194\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[2m1191 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[2m# this function, and just call forward.\u001b[0m                                           \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1192 \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._forward_hooks \u001b[95mor\u001b[0m \u001b[96mself\u001b[0m._forward_pre_hooks \u001b[95mo\u001b[0m  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1193 \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[0m1194 \u001b[2m│   │   │   \u001b[0m\u001b[94mreturn\u001b[0m forward_call(*\u001b[96minput\u001b[0m, **kwargs)                                         \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1195 \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[2m1196 \u001b[0m\u001b[2m│   │   \u001b[0mfull_backward_hooks, non_full_backward_hooks = [], []                             \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1197 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mif\u001b[0m \u001b[96mself\u001b[0m._backward_hooks \u001b[95mor\u001b[0m _global_backward_hooks:                                \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[2;33m/home/ubuntu/env/lib/python3.8/site-packages/accelerate/\u001b[0m\u001b[1;33mhooks.py\u001b[0m:\u001b[94m156\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[2m153 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[94mwith\u001b[0m torch.no_grad():                                                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m154 \u001b[0m\u001b[2m│   │   │   │   \u001b[0moutput = old_forward(*args, **kwargs)                                      \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m155 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94melse\u001b[0m:                                                                              \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m156 \u001b[2m│   │   │   \u001b[0moutput = old_forward(*args, **kwargs)                                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m157 \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[2m158 \u001b[0m\u001b[2m│   \u001b[0m                                                                                       \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m159 \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/home/ubuntu/transformers/src/transformers/models/opt/\u001b[0m\u001b[1;33mmodeling_opt.py\u001b[0m:\u001b[94m172\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[2m 169 \u001b[0m\u001b[2m│   │   \u001b[0mbsz, tgt_len, _ = hidden_states.size()                                            \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m 170 \u001b[0m\u001b[2m│   │   \u001b[0m                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m 171 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[2m# get query proj\u001b[0m                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 172 \u001b[2m│   │   \u001b[0mquery_states = \u001b[96mself\u001b[0m.q_proj(hidden_states) * \u001b[96mself\u001b[0m.scaling                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m 173 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[2m# get key, value proj\u001b[0m                                                             \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m 174 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mif\u001b[0m is_cross_attention \u001b[95mand\u001b[0m past_key_value \u001b[95mis\u001b[0m \u001b[95mnot\u001b[0m \u001b[94mNone\u001b[0m:                             \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m 175 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[2m# reuse k,v, cross_attentions\u001b[0m                                                 \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[2;33m/home/ubuntu/env/lib/python3.8/site-packages/torch/nn/modules/\u001b[0m\u001b[1;33mmodule.py\u001b[0m:\u001b[94m1194\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[2m1191 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[2m# this function, and just call forward.\u001b[0m                                           \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1192 \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._forward_hooks \u001b[95mor\u001b[0m \u001b[96mself\u001b[0m._forward_pre_hooks \u001b[95mo\u001b[0m  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1193 \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[0m1194 \u001b[2m│   │   │   \u001b[0m\u001b[94mreturn\u001b[0m forward_call(*\u001b[96minput\u001b[0m, **kwargs)                                         \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1195 \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[2m1196 \u001b[0m\u001b[2m│   │   \u001b[0mfull_backward_hooks, non_full_backward_hooks = [], []                             \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1197 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mif\u001b[0m \u001b[96mself\u001b[0m._backward_hooks \u001b[95mor\u001b[0m _global_backward_hooks:                                \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[2;33m/home/ubuntu/env/lib/python3.8/site-packages/accelerate/\u001b[0m\u001b[1;33mhooks.py\u001b[0m:\u001b[94m156\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[2m153 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[94mwith\u001b[0m torch.no_grad():                                                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m154 \u001b[0m\u001b[2m│   │   │   │   \u001b[0moutput = old_forward(*args, **kwargs)                                      \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m155 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94melse\u001b[0m:                                                                              \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m156 \u001b[2m│   │   │   \u001b[0moutput = old_forward(*args, **kwargs)                                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m157 \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[2m158 \u001b[0m\u001b[2m│   \u001b[0m                                                                                       \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m159 \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/home/ubuntu/env/lib/python3.8/site-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[0mCUDA error: CUBLAS_STATUS_EXECUTION_FAILED when calling `\u001b[1;35mcublasLtMatmul\u001b[0m\u001b[1m(\u001b[0m ltHandle, \n",
       "\u001b[1;35mcomputeDesc.descriptor\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m, &alpha_val, mat1_ptr, \u001b[1;35mAdesc.descriptor\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m, mat2_ptr, \u001b[1;35mBdesc.descriptor\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m, &beta_val, \n",
       "result_ptr, \u001b[1;35mCdesc.descriptor\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m, result_ptr, \u001b[1;35mCdesc.descriptor\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m, &heuristicResult.algo, \u001b[1;35mworkspace.data_ptr\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m, \n",
       "workspaceSize, at::cuda::\u001b[1;35mgetCurrentCUDAStream\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m`\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = []\n",
    "pred = []\n",
    "with torch.inference_mode():\n",
    "    for sample in tqdm.tqdm(samples):\n",
    "        labels.append(sample[\"label\"])\n",
    "        input_ids = tokenizer(sample[\"text\"], return_tensors=\"pt\",).input_ids.to(\"cuda\")\n",
    "        outputs = model.generate(input_ids,max_new_tokens=8)\n",
    "        pred.append(tokenizer.decode(outputs[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "708cd824",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " '<pad> Contradiction</s>',\n",
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       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
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       " '<pad> Entailment</s>',\n",
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       " '<pad> Contradiction</s>',\n",
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       " '<pad> Contradiction</s>',\n",
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       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
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       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
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       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32f3b8fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = [p[5:][:-4].strip() for p in pred]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d349880",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Contradiction', 'Entailment'}\n"
     ]
    }
   ],
   "source": [
    "print(set(pred))\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b9a6d6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'Contradiction': 264, 'Entailment': 236})"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Counter(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6fbbb1d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_dict = {}\n",
    "for _id,pred_x in zip(data, pred):\n",
    "    prediction_dict[str(_id)] = {\"Prediction\":pred_x}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fdfcff67",
   "metadata": {},
   "outputs": [],
   "source": [
    "json.dump(prediction_dict, open(\"results.json\",'w'),indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e87cce1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  adding: results.json (deflated 73%)\n"
     ]
    }
   ],
   "source": [
    "!zip results_xxl_train_1024_2_7e_6_Dev_repro.zip results.json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "37a17ffd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'9f978634-637c-472f-a588-6f4bb2fb121f': {'Prediction': 'Contradiction'},\n",
       " '20b34e62-97c2-4ca0-bb1d-7824dab0b8bb': {'Prediction': 'Entailment'},\n",
       " '893a5337-2aa9-4a87-a020-4c2f03cd4aea': {'Prediction': 'Entailment'},\n",
       " 'd401affc-f081-4eee-bd61-d109cc88f6de': {'Prediction': 'Entailment'},\n",
       " '791790a6-187b-4e4b-be5f-9e5304e9ec2c': {'Prediction': 'Contradiction'},\n",
       " 'b95b7438-ec16-4d4d-826d-5891e7982b36': {'Prediction': 'Entailment'},\n",
       " '4988cb16-7dbb-4847-84e0-4a7957b32c72': {'Prediction': 'Entailment'},\n",
       " 'e244fc3a-53b3-4158-99c5-a45afc726af6': {'Prediction': 'Contradiction'},\n",
       " '56530063-b408-47f2-8421-6be825f5559c': {'Prediction': 'Entailment'},\n",
       " 'd3379655-55b7-4e58-88c2-c3cd3e8cb557': {'Prediction': 'Contradiction'},\n",
       " '7bf988b4-5e6f-41c4-bef6-7b3549dd58d9': {'Prediction': 'Contradiction'},\n",
       " '84915e35-a8c9-4d26-ad09-4b1df48a6df8': {'Prediction': 'Entailment'},\n",
       " '64df451c-f868-49e6-9b4e-a325e62ce837': {'Prediction': 'Entailment'},\n",
       " 'dc4765a3-6168-4594-969b-3ce5ea7dc02a': {'Prediction': 'Contradiction'},\n",
       " '9c2ca035-d531-4d44-876e-6ed9f2d3f76b': {'Prediction': 'Entailment'},\n",
       " '7238ddd4-eade-4b88-8933-0c3df5be9875': {'Prediction': 'Contradiction'},\n",
       " 'aeddc485-09b1-4ddc-9a45-3a8bf0bec2c3': {'Prediction': 'Entailment'},\n",
       " '204d5293-d6e5-452a-8961-3b76d6be35c9': {'Prediction': 'Entailment'},\n",
       " '4ed1c5fd-58e5-4fe8-97b1-59ed509ccb10': {'Prediction': 'Entailment'},\n",
       " '6d0812b9-f9bd-4cd6-82aa-9c272eba23be': {'Prediction': 'Contradiction'},\n",
       " 'fd0fa999-2f1c-400d-977d-2d45dd9c358c': {'Prediction': 'Contradiction'},\n",
       " '571d4650-e64b-4cb2-b719-0d7064e51745': {'Prediction': 'Contradiction'},\n",
       " '4437aa5b-4b4e-4eb4-a258-e7f17dc8f363': {'Prediction': 'Contradiction'},\n",
       " 'd7abbd7d-a767-49c0-bc99-1c28074eac21': {'Prediction': 'Contradiction'},\n",
       " '54c7e880-a1bf-4206-9573-1c994932ba15': {'Prediction': 'Entailment'},\n",
       " 'f9f0ba74-16e0-4790-afa0-b7616b256e5e': {'Prediction': 'Entailment'},\n",
       " '412583db-ce19-451d-a804-3837e4dcaea3': {'Prediction': 'Contradiction'},\n",
       " 'bca4c94d-4a49-4e05-82ed-0d5c9707685b': {'Prediction': 'Entailment'},\n",
       " '97acf304-2707-4a1b-81e7-339fde2ed0ee': {'Prediction': 'Contradiction'},\n",
       " '21a5f296-5b3c-4860-be64-77ffb8f76501': {'Prediction': 'Entailment'},\n",
       " '8d27bb53-e66e-4eab-bea4-0597fd5760b5': {'Prediction': 'Entailment'},\n",
       " 'dc60304d-6fd2-4b12-9108-b5ce78454d42': {'Prediction': 'Contradiction'},\n",
       " '328417f9-8f45-4114-82d7-9396b6bc578e': {'Prediction': 'Contradiction'},\n",
       " '40d84c7b-e126-4d6f-9588-eb9c385ca716': {'Prediction': 'Contradiction'},\n",
       " 'b685be77-9c55-4948-99cc-2a249739a233': {'Prediction': 'Entailment'},\n",
       " '5fbee4f6-43fc-45a7-88c4-3dcd5ff21053': {'Prediction': 'Entailment'},\n",
       " '8127d46a-3a48-4173-8d00-75e949755a74': {'Prediction': 'Contradiction'},\n",
       " '576967ff-d2bb-4d1c-b2a1-8516ceb7a519': {'Prediction': 'Contradiction'},\n",
       " 'fd334e71-2bbe-4138-b578-2ab848686045': {'Prediction': 'Entailment'},\n",
       " 'a72ddea5-ee4b-4895-bd8c-10b4a143c3f8': {'Prediction': 'Contradiction'},\n",
       " '6c29e8c1-36ca-4e6e-b4dc-f1e91b98e546': {'Prediction': 'Contradiction'},\n",
       " 'd1118512-9c0b-4480-ab67-d4e0c7de390d': {'Prediction': 'Contradiction'},\n",
       " 'd35e9b96-20fc-4778-a7db-e87c861c4938': {'Prediction': 'Entailment'},\n",
       " '25b01c06-612c-4cfb-b30b-50ebe4d98750': {'Prediction': 'Entailment'},\n",
       " '1a76bce8-07e2-4b30-adc8-3c095f0d62b3': {'Prediction': 'Contradiction'},\n",
       " '23f84ae0-fd01-4799-8872-118e22a015e8': {'Prediction': 'Contradiction'},\n",
       " 'c2aea351-6ce5-4d26-9ba5-2632c6367b93': {'Prediction': 'Entailment'},\n",
       " '388cc99c-4cb3-4ff9-9496-07f4a6bf6ac7': {'Prediction': 'Contradiction'},\n",
       " '5af6aa8c-69f5-44a5-9c7c-6c94a0fda6e8': {'Prediction': 'Contradiction'},\n",
       " '455d059c-c3c4-4a09-be0d-a14b9568a0d2': {'Prediction': 'Entailment'},\n",
       " '6d37ed53-a6e4-403e-8b93-9954a6486dbc': {'Prediction': 'Entailment'},\n",
       " 'e530eed4-ee8b-4a7f-bb36-5ce9ea7ddebe': {'Prediction': 'Contradiction'},\n",
       " '41a0138d-7084-40d9-99ae-24db9e84acce': {'Prediction': 'Entailment'},\n",
       " '11cb71ed-1304-42f0-b416-3679c3e9e1b3': {'Prediction': 'Contradiction'},\n",
       " '6ddf4b5b-69e2-46e1-b032-69c3173fd8ef': {'Prediction': 'Entailment'},\n",
       " '9349c7c8-7771-4dfc-8981-3c8d84ac5236': {'Prediction': 'Entailment'},\n",
       " '4e91cee2-910e-4f08-8a85-1f13545391c3': {'Prediction': 'Contradiction'},\n",
       " '18c281a1-43bd-47f5-9534-feddfcf0a282': {'Prediction': 'Contradiction'},\n",
       " 'dd3d2183-d062-41ed-af63-ae475f2246ec': {'Prediction': 'Contradiction'},\n",
       " '711fdd40-52ec-401c-90fc-5a11de547ab6': {'Prediction': 'Entailment'},\n",
       " '49b3b463-a496-404a-a3a5-07cd7d4aaa92': {'Prediction': 'Entailment'},\n",
       " '43b9c2c8-0785-4885-901b-91a1f893858f': {'Prediction': 'Entailment'},\n",
       " '43989f85-a059-4dc7-a805-16e081706b2f': {'Prediction': 'Entailment'},\n",
       " '282a33db-aed6-4daa-bea6-299f424914bb': {'Prediction': 'Contradiction'},\n",
       " '646691ba-ad29-479f-81dc-131d9710c23b': {'Prediction': 'Entailment'},\n",
       " 'a0c1da21-1e1f-493b-81e9-4ef133c20090': {'Prediction': 'Contradiction'},\n",
       " '439b1eca-f583-4a2b-bd8e-0baae97b4c15': {'Prediction': 'Contradiction'},\n",
       " 'e12e5ed1-9a64-4183-9b9d-419e6679ab3d': {'Prediction': 'Entailment'},\n",
       " '73849f1e-31c6-40c5-ab5c-35d0990ceac2': {'Prediction': 'Entailment'},\n",
       " 'f53c5344-36d7-459f-9f84-300e19decced': {'Prediction': 'Entailment'},\n",
       " '77fd9a89-4d9b-40ab-b4ad-fcf554ca68d2': {'Prediction': 'Entailment'},\n",
       " '99433bba-fce5-4b0a-a076-1f699b51472d': {'Prediction': 'Entailment'},\n",
       " '135763e3-70fd-478d-991c-717d6ed55bb3': {'Prediction': 'Entailment'},\n",
       " '6cbfc99b-6531-412f-bd30-b4a803983831': {'Prediction': 'Entailment'},\n",
       " '6a3053ef-4495-4c18-9da7-68cf5ed254e0': {'Prediction': 'Contradiction'},\n",
       " '217b0362-9dd0-4f10-8ca5-c097688bb738': {'Prediction': 'Contradiction'},\n",
       " 'd88235dd-c37b-4971-9c1f-42b282ff41c1': {'Prediction': 'Contradiction'},\n",
       " 'e6bbed17-0fdf-4008-b56e-4d901490ce1a': {'Prediction': 'Entailment'},\n",
       " 'f1f9d4b4-4877-49ad-a2e2-f53efbe40ef9': {'Prediction': 'Entailment'},\n",
       " '8393ad3d-8521-4316-b69e-64910d8af5e7': {'Prediction': 'Contradiction'},\n",
       " 'cca11232-84c4-44e1-89f1-b57b28643dce': {'Prediction': 'Contradiction'},\n",
       " 'cb36e435-6dc9-4e7b-939b-9da6ceef2a49': {'Prediction': 'Contradiction'},\n",
       " '7ce21c0f-ad55-4ff7-a180-5e418fe9afc8': {'Prediction': 'Entailment'},\n",
       " '42c38d71-6a83-4967-ad81-ec38c6b5aafd': {'Prediction': 'Entailment'},\n",
       " '6c5ebc06-00c4-4cf5-8912-93972ed5a49b': {'Prediction': 'Entailment'},\n",
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       " 'c43583e4-da5f-42b3-ad3d-7ba8b6b28f2b': {'Prediction': 'Contradiction'},\n",
       " '094e7aec-88db-4ad0-b995-ece8587fd48b': {'Prediction': 'Contradiction'},\n",
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       " '58fa0ef4-0355-42bc-8378-fef98752b05e': {'Prediction': 'Contradiction'},\n",
       " 'b033d45a-c039-42de-a073-3c051c6f1e3c': {'Prediction': 'Entailment'},\n",
       " '75c55f70-aa52-4794-aae7-3a355f1a76e9': {'Prediction': 'Contradiction'},\n",
       " '070ba2da-932a-4a62-861e-3f2c89630b4d': {'Prediction': 'Contradiction'},\n",
       " '76d498ce-037c-4f7e-8cbb-450d8f6019ec': {'Prediction': 'Entailment'},\n",
       " 'dfd8e736-5ef2-4d36-b124-9382e72f982d': {'Prediction': 'Entailment'},\n",
       " 'a4a2c0ba-3fa9-4fad-9258-7e517ae85e25': {'Prediction': 'Entailment'},\n",
       " 'e8e3fedb-075d-440e-bf2e-baec76f96cca': {'Prediction': 'Entailment'},\n",
       " 'b53a2698-dfb0-416d-9f3e-2e800a360b3e': {'Prediction': 'Entailment'},\n",
       " 'cd067ee2-ed1d-403b-aa50-d61a3399fda3': {'Prediction': 'Contradiction'},\n",
       " '8b50bdc1-1722-403b-a854-adeaacd02acb': {'Prediction': 'Contradiction'}}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_dict"
   ]
  },
  {
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   "execution_count": null,
   "id": "ea80658b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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