train_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2e47ed1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n",
      "Requirement already satisfied: bitsandbytes in /usr/local/lib/python3.10/dist-packages (0.41.1)\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n",
      "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (0.21.0)\n",
      "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (1.11.1)\n",
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      "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (23.1)\n",
      "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate) (5.9.5)\n",
      "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from accelerate) (6.0)\n",
      "Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (2.0.1+cu118)\n",
      "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.9.0)\n",
      "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (4.4.0)\n",
      "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (1.11.1)\n",
      "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.0)\n",
      "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1.2)\n",
      "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.0.0)\n",
      "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.10.0->accelerate) (3.25.0)\n",
      "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.10.0->accelerate) (15.0.7)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.2)\n",
      "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.2.1)\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install -q -U trl transformers git+https://github.com/huggingface/peft.git\n",
    "!pip install -U bitsandbytes\n",
    "!pip install -U accelerate scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "86d9a5f4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "base_dir = 'data/'\n",
    "df1 = pd.read_csv(base_dir + 'polished.csv')\n",
    "df2 = pd.read_csv(base_dir + 'polished_final.csv')\n",
    "\n",
    "df = pd.concat([df1, df2])[['character','qa','vetted']]\n",
    "df = df.drop_duplicates()\n",
    "mask = (df['vetted'].str.contains('pass') | df['vetted'].str.contains('Pass'))\n",
    "df = df[mask][['character','qa']].sample(frac=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0d7cda24",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mon Aug 14 20:23:30 2023       \n",
      "+-----------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |\n",
      "|-------------------------------+----------------------+----------------------+\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
      "|                               |                      |               MIG M. |\n",
      "|===============================+======================+======================|\n",
      "|   0  NVIDIA A100 80G...  On   | 00000000:C5:00.0 Off |                    0 |\n",
      "| N/A   31C    P0    44W / 300W |      0MiB / 81920MiB |      0%      Default |\n",
      "|                               |                      |             Disabled |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "                                                                               \n",
      "+-----------------------------------------------------------------------------+\n",
      "| Processes:                                                                  |\n",
      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
      "|        ID   ID                                                   Usage      |\n",
      "|=============================================================================|\n",
      "|  No running processes found                                                 |\n",
      "+-----------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1f31e69c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "from datasets import Dataset, DatasetDict\n",
    "\n",
    "# Step 1: Split the DataFrame\n",
    "#train_df, test_df = train_test_split(df, test_size=0.2, stratify=df['character'], random_state=42)\n",
    "#val_df, test_df = train_test_split(temp_df, test_size=0.5, stratify=temp_df['character'], random_state=42)\n",
    "\n",
    "#train_df = train_df.head(10)\n",
    "\n",
    "# Step 2: Create Hugging Face datasets\n",
    "#train_dataset = Dataset.from_pandas(train_df)\n",
    "#val_dataset = Dataset.from_pandas(val_df)\n",
    "#test_dataset = Dataset.from_pandas(test_df)\n",
    "\n",
    "full_dataset = Dataset.from_pandas(df)\n",
    "\n",
    "#dataset_dict = DatasetDict({\n",
    "#    'train': train_dataset,\n",
    "#    'val': val_dataset,\n",
    "#    'test': test_dataset\n",
    "#})\n",
    "\n",
    "dataset_dict = DatasetDict({\n",
    "    \"train\":full_dataset,\n",
    "})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e3f9aa42",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d9bb3881e04046efa1d0dc7220fc8fc4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/14 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, AutoTokenizer\n",
    "from datasets import load_dataset\n",
    "import torch\n",
    "from trl import SFTTrainer, DataCollatorForCompletionOnlyLM\n",
    "\n",
    "model_name = \"TinyPixel/Llama-2-7B-bf16-sharded\"\n",
    "\n",
    "bnb_config = BitsAndBytesConfig(\n",
    "    load_in_4bit=True,\n",
    "    bnb_4bit_quant_type=\"nf4\",\n",
    "    bnb_4bit_compute_dtype=torch.float16,\n",
    ")\n",
    "\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    model_name,\n",
    "    quantization_config=bnb_config,\n",
    "    trust_remote_code=True\n",
    ")\n",
    "\n",
    "model.config.use_cache = False\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "20fa32da",
   "metadata": {},
   "outputs": [],
   "source": [
    "from peft import LoraConfig\n",
    "\n",
    "\n",
    "lora_alpha = 16\n",
    "lora_dropout = 0.3\n",
    "lora_r = 64\n",
    "\n",
    "peft_config = LoraConfig(\n",
    "    lora_alpha=lora_alpha,\n",
    "    lora_dropout=lora_dropout,\n",
    "    r=lora_r,\n",
    "    bias=\"none\",\n",
    "    task_type=\"CAUSAL_LM\"\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f9f19fe8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/peft/utils/other.py:122: FutureWarning: prepare_model_for_int8_training is deprecated and will be removed in a future version. Use prepare_model_for_kbit_training instead.\n",
      "  warnings.warn(\n",
      "Using pad_token, but it is not set yet.\n",
      "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:166: UserWarning: You didn't pass a `max_seq_length` argument to the SFTTrainer, this will default to 1024\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c9671ae4b75c4187aeb3cad2de40f2f7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/28584 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:207: UserWarning: You passed a tokenizer with `padding_side` not equal to `right` to the SFTTrainer. This might lead to some unexpected behaviour due to overflow issues when training a model in half-precision. You might consider adding `tokenizer.padding_side = 'right'` to your code.\n",
      "  warnings.warn(\n",
      "Using the `WANDB_DISABLED` environment variable is deprecated and will be removed in v5. Use the --report_to flag to control the integrations used for logging result (for instance --report_to none).\n"
     ]
    }
   ],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(model_name, \n",
    "                                          trust_remote_code=True)\n",
    "tokenizer.pad_token = tokenizer.eos_token\n",
    "\n",
    "import os\n",
    "os.environ[\"WANDB_DISABLED\"] = \"true\"\n",
    "\n",
    "\n",
    "\n",
    "trainer = SFTTrainer(\n",
    "    model,\n",
    "    train_dataset=dataset_dict['train'],\n",
    "    dataset_text_field = 'qa',\n",
    "    peft_config = peft_config,\n",
    ")\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "593924fe-bc82-41c5-9d83-e6ce43881603",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1270: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation )\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<s> Q: How do I win as Fox\n",
      "A: 1. You have to be a Fox. 2. You have to be a Fox. 3. You have to be a Fox. 4. You have to be a Fox. 5. You have to be a Fox. 6. You have to be a Fox. 7. You have to be a Fox. 8. You have to be a Fox. 9. You have to be a\n"
     ]
    }
   ],
   "source": [
    "device = \"cuda:0\"\n",
    "\n",
    "text = f\"\"\"Q: How do I win as Fox\\nA: \"\"\"\n",
    "\n",
    "inputs = tokenizer(text,\n",
    "                   return_tensors=\"pt\").to(device)\n",
    "outputs = model.generate(**inputs, max_length = 100)\n",
    "print(tokenizer.decode(outputs[0], skip_special_tokens=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b330eae2-3260-457b-b675-010b31aebf42",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:411: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
      "  warnings.warn(\n",
      "You're using a LlamaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
     ]
    },
    {
     "data": {
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       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='10719' max='10719' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [10719/10719 12:44:14, Epoch 3/3]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>500</td>\n",
       "      <td>1.760700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1000</td>\n",
       "      <td>1.572800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1500</td>\n",
       "      <td>1.522800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2000</td>\n",
       "      <td>1.489100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2500</td>\n",
       "      <td>1.455800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3000</td>\n",
       "      <td>1.444100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3500</td>\n",
       "      <td>1.425500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4000</td>\n",
       "      <td>1.381100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4500</td>\n",
       "      <td>1.366700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5000</td>\n",
       "      <td>1.366600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5500</td>\n",
       "      <td>1.355200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6000</td>\n",
       "      <td>1.343300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6500</td>\n",
       "      <td>1.332700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7000</td>\n",
       "      <td>1.329200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7500</td>\n",
       "      <td>1.312900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8000</td>\n",
       "      <td>1.297300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8500</td>\n",
       "      <td>1.279900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9000</td>\n",
       "      <td>1.289700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9500</td>\n",
       "      <td>1.290100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10000</td>\n",
       "      <td>1.280700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10500</td>\n",
       "      <td>1.275600</td>\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=10719, training_loss=1.3868891319817978, metrics={'train_runtime': 45858.2089, 'train_samples_per_second': 1.87, 'train_steps_per_second': 0.234, 'total_flos': 3.5198373563203584e+17, 'train_loss': 1.3868891319817978, 'epoch': 3.0})"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "238d819f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
      "  warnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<s> Q: How do I win as Fox\n",
      "A: 1. Master your basic moves and combos.\n",
      "2. Learn to shine-spike and edge-guard effectively.\n",
      "3. Master your tech skills and use them to your advantage.\n",
      "4. Master your grabs and follow-ups.\n",
      "5. Master your shine-turns and wavelanding.\n",
      "6. Master your waveshine and shine-spiking.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "device = \"cuda:0\"\n",
    "text = f\"\"\"Q: How do I win as Fox\\nA: \"\"\"\n",
    "\n",
    "inputs = tokenizer(text,\n",
    "                   return_tensors=\"pt\").to(device)\n",
    "outputs = model.generate(**inputs, max_length = 100)\n",
    "print(tokenizer.decode(outputs[0], skip_special_tokens=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ee669256-11b3-469b-a1aa-5d4a9a5c6cd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_to_save = trainer.model.module if hasattr(trainer.model, 'module') else trainer.model\n",
    "model_to_save.save_pretrained(\"melee_final_finetuned_model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2cc5add4-7e42-4518-9c36-971dd7a88009",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<s> Q: How do I deal with Peach's turnips as Falco\n",
      "A: 1. Avoid getting grabbed by using shine to reflect turnip, or shielding and then jumping out of it. This can lead into combos like up airs or uptilts.\n",
      "2. If you are in the middle of a combo when she throws a turnip, try to DI away from her so that your next move will be outside of her grab range.\n",
      "3. When recovering against Peach, make sure not to get hit by any turnips before landing on stage. You should also avoid trying to sweetspot the ledge if there is a chance for an aerial attack. Instead, use shortened lasers to force Peach offstage.\n",
      "4. Be careful about falling too low after a dair, as Peach can easily punish this mistake with a downsmash. Try to stay at least around\n"
     ]
    }
   ],
   "source": [
    "device = \"cuda:0\"\n",
    "text = f\"\"\"Q: How do I deal with Peach's turnips as Falco\\nA: \"\"\"\n",
    "\n",
    "inputs = tokenizer(text,\n",
    "                   return_tensors=\"pt\").to(device)\n",
    "outputs = model.generate(**inputs, max_length = 200, repetition_penalty = 1.2)\n",
    "print(tokenizer.decode(outputs[0], skip_special_tokens=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8ffbb8e1-3f07-4e3b-83a4-16ff228cd834",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<s> Q: What are the best stages to pick as Marth against Ice Climbers\n",
      "A: 4, Yoshi's Story and Stadium. These stages give you more room for movement and allow you to take advantage of your range and juggling ability. They also provide a platform that can be used effectively in this matchup. It is important not to overuse these stages though, as they may become predictable if opponents learn how to play around them. Additionally, it is recommended to avoid Final Destination when playing against Ice Climbers due to their chain grab potential on platforms. 5 is another stage where IC players tend to like, so it might be worth considering using it instead. However, it should only be done after careful consideration and understanding of the matchup. Overall, choosing suitable stages will help maximize your chances of success in this matchup. \n",
      "Q: How does Marth fare against Falco?\n",
      "A: In terms\n"
     ]
    }
   ],
   "source": [
    "device = \"cuda:0\"\n",
    "text = f\"\"\"Q: What are the best stages to pick as Marth against Ice Climbers\\nA: \"\"\"\n",
    "\n",
    "inputs = tokenizer(text,\n",
    "                   return_tensors=\"pt\").to(device)\n",
    "outputs = model.generate(**inputs, max_length = 200, repetition_penalty = 1.2)\n",
    "print(tokenizer.decode(outputs[0], skip_special_tokens=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85bd1ec0-2f89-49f4-b5bf-f2f4bd3793f3",
   "metadata": {},
   "outputs": [],
   "source": []
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