{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "540de0f1-8363-4185-b88c-2cf165aa5b91",
   "metadata": {
    "id": "540de0f1-8363-4185-b88c-2cf165aa5b91"
   },
   "source": [
    "# Lightweight Fine-Tuning Project"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "618bc1e3-b7c4-4d60-a840-c04d0d92e3d2",
   "metadata": {
    "id": "618bc1e3-b7c4-4d60-a840-c04d0d92e3d2"
   },
   "source": [
    "TODO: In this cell, describe your choices for each of the following\n",
    "\n",
    "* PEFT technique:\n",
    "* Model:\n",
    "* Evaluation approach:\n",
    "* Fine-tuning dataset:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc6aadea-517d-4ac3-908d-4e60dbbe987b",
   "metadata": {
    "id": "cc6aadea-517d-4ac3-908d-4e60dbbe987b"
   },
   "source": [
    "## Loading and Evaluating a Foundation Model\n",
    "\n",
    "TODO: In the cells below, load your chosen pre-trained Hugging Face model and evaluate its performance prior to fine-tuning. This step includes loading an appropriate tokenizer and dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1660d997",
   "metadata": {
    "id": "1660d997"
   },
   "source": [
    "# 1. Installation\n",
    "\n",
    "Below we install the necessary packages for this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8qA7EH0umAsX",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8qA7EH0umAsX",
    "outputId": "c8a26717-3ca6-489b-e022-b86154b58adc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
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      "Requirement already satisfied: six>=1.5 in /home/student/.local/lib/python3.10/site-packages (from python-dateutil>=2.8.2->pandas->datasets==3.2.0) (1.16.0)\n",
      "Installing collected packages: tqdm, threadpoolctl, requests, joblib, scikit-learn, huggingface-hub, datasets, evaluate\n",
      "  Attempting uninstall: tqdm\n",
      "    Found existing installation: tqdm 4.66.2\n",
      "    Uninstalling tqdm-4.66.2:\n",
      "      Successfully uninstalled tqdm-4.66.2\n",
      "\u001b[33m  WARNING: The script tqdm is installed in '/home/student/.local/bin' which is not on PATH.\n",
      "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\u001b[33m\n",
      "\u001b[0m  Attempting uninstall: requests\n",
      "    Found existing installation: requests 2.31.0\n",
      "    Uninstalling requests-2.31.0:\n",
      "      Successfully uninstalled requests-2.31.0\n",
      "  Attempting uninstall: huggingface-hub\n",
      "    Found existing installation: huggingface-hub 0.21.4\n",
      "    Uninstalling huggingface-hub-0.21.4:\n",
      "      Successfully uninstalled huggingface-hub-0.21.4\n",
      "\u001b[33m  WARNING: The script huggingface-cli is installed in '/home/student/.local/bin' which is not on PATH.\n",
      "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33m  WARNING: The script datasets-cli is installed in '/home/student/.local/bin' which is not on PATH.\n",
      "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33m  WARNING: The script evaluate-cli is installed in '/home/student/.local/bin' which is not on PATH.\n",
      "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\u001b[33m\n",
      "\u001b[0mSuccessfully installed datasets-3.2.0 evaluate-0.4.3 huggingface-hub-0.27.0 joblib-1.4.2 requests-2.32.3 scikit-learn-1.6.0 threadpoolctl-3.5.0 tqdm-4.67.1\n"
     ]
    }
   ],
   "source": [
    "# You will need to choose \"Kernel > Restart Kernel\" from the menu after executing this cell\n",
    "\n",
    "!pip install evaluate scikit-learn \"datasets==3.2.0\" bitsandbytes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8920ddfd",
   "metadata": {
    "id": "8920ddfd"
   },
   "source": [
    "# 2. Imports\n",
    "\n",
    "In this section, we import the libraries and modules we will need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4ffe2951",
   "metadata": {
    "id": "4ffe2951"
   },
   "outputs": [],
   "source": [
    "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
    "from datasets import load_dataset\n",
    "import pandas as pd\n",
    "import torch\n",
    "import numpy as np\n",
    "\n",
    "from peft import LoraConfig, TaskType, get_peft_model, AutoPeftModelForSequenceClassification\n",
    "from transformers import DataCollatorWithPadding, TrainingArguments, Trainer, BitsAndBytesConfig\n",
    "from huggingface_hub import login"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "353bcfbd-7272-49b9-b429-06ffcea54420",
   "metadata": {
    "id": "353bcfbd-7272-49b9-b429-06ffcea54420"
   },
   "source": [
    "# 3. Tokenizer Initialization\n",
    "\n",
    "We load the GPT-2 tokenizer here, specifying a maximum length for the tokens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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    "id": "575464d1-566c-4bf6-8e4e-b914356c6ce1",
    "outputId": "fd06f6b0-50c2-408e-d8d7-cd68e3d21aaf"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/student/.local/lib/python3.10/site-packages/huggingface_hub/file_download.py:795: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "MODEL_NAME = \"gpt2\"\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, max_length=1024)\n",
    "tokenizer.pad_token = tokenizer.eos_token  # Set pad token to the EOS token"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdNWtZ-_FEjR",
   "metadata": {
    "id": "fdNWtZ-_FEjR"
   },
   "source": [
    "# 4. Load and Preview Dataset\n",
    "\n",
    "We load the **sms_spam** dataset and split it into training and test sets. We then display the raw dataset for inspection."
   ]
  },
  {
   "cell_type": "code",
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    "outputId": "74df2830-c0db-4089-a4cf-041c86421840",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['sms', 'label'],\n",
       "        num_rows: 4459\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['sms', 'label'],\n",
       "        num_rows: 1115\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_name = \"sms_spam\"\n",
    "\n",
    "raw_datasets = load_dataset(dataset_name, split=\"train\").train_test_split(\n",
    "    test_size=0.2, shuffle=True, seed=23\n",
    ")\n",
    "\n",
    "# Display basic dataset info\n",
    "raw_datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "vvyH2xotUXVw",
   "metadata": {
    "id": "vvyH2xotUXVw"
   },
   "source": [
    "# 5. Tokenize the Dataset\n",
    "\n",
    "Here, we define a `tokenize_func` function and apply it to the dataset. We remove the original 'sms' column to keep the dataset clean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a6qLbG6cUbIy",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 220,
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    },
    "id": "a6qLbG6cUbIy",
    "outputId": "51200a92-afbb-4912-dc7b-94629f9deb58"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8af0e32dd8b24bb3ac686fff1fd39d03",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/1115 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'train': Dataset({\n",
       "     features: ['label', 'input_ids', 'attention_mask'],\n",
       "     num_rows: 4459\n",
       " }),\n",
       " 'test': Dataset({\n",
       "     features: ['label', 'input_ids', 'attention_mask'],\n",
       "     num_rows: 1115\n",
       " })}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def tokenize_func(element):\n",
    "    return tokenizer(\n",
    "        element['sms'],\n",
    "        padding=\"max_length\",\n",
    "        truncation=True\n",
    "    )\n",
    "\n",
    "splits = [\"train\", \"test\"]\n",
    "tokenized_datasets = {}\n",
    "\n",
    "for split in splits:\n",
    "    tokenized_datasets[split] = raw_datasets[split].map(\n",
    "        tokenize_func,\n",
    "        batched=True,\n",
    "        remove_columns=[\"sms\"]\n",
    "    )\n",
    "\n",
    "# Inspect the resulting tokenized dataset\n",
    "tokenized_datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5727744e",
   "metadata": {
    "id": "5727744e"
   },
   "source": [
    "# 6. Quick Look at Tokenized Data\n",
    "\n",
    "Convert the first few examples to a DataFrame to see how they look after tokenization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4b8da8d5",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "4b8da8d5",
    "outputId": "742430d3-df03-47cb-e4e9-966455533d97"
   },
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>label</th>\n",
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       "   label                                          input_ids  \\\n",
       "0      1  [25383, 534, 5175, 838, 285, 9998, 30, 10133, ...   \n",
       "1      0  [7594, 220, 1222, 2528, 26, 2, 5, 13655, 26, 8...   \n",
       "2      0  [19926, 314, 423, 6497, 510, 257, 14507, 393, ...   \n",
       "3      0  [18565, 306, 8508, 319, 428, 1323, 290, 340, 1...   \n",
       "4      0  [18690, 986, 198, 50256, 50256, 50256, 50256, ...   \n",
       "\n",
       "                                      attention_mask  \n",
       "0  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...  \n",
       "1  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, ...  \n",
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       "4  [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(tokenized_datasets[\"train\"][:5])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c306fa8",
   "metadata": {
    "id": "7c306fa8"
   },
   "source": [
    "# 7. Load Pretrained Model\n",
    "\n",
    "We load a GPT-2 based model configured for sequence classification with 2 labels (spam / not spam).\n",
    "\n",
    "We will also use normalized float 4 adn the BitsAndBytes library to quantize our model for better performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "78f0bf5e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 570,
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    },
    "id": "78f0bf5e",
    "outputId": "7a992231-0a2d-4de6-bcbc-6fd34ca81e8a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/student/.local/lib/python3.10/site-packages/huggingface_hub/file_download.py:795: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
      "  warnings.warn(\n",
      "Some weights of GPT2ForSequenceClassification were not initialized from the model checkpoint at gpt2 and are newly initialized: ['score.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "memory footprint 255544368\n",
      "GPT2ForSequenceClassification(\n",
      "  (transformer): GPT2Model(\n",
      "    (wte): Embedding(50257, 768)\n",
      "    (wpe): Embedding(1024, 768)\n",
      "    (drop): Dropout(p=0.1, inplace=False)\n",
      "    (h): ModuleList(\n",
      "      (0-11): 12 x GPT2Block(\n",
      "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
      "        (attn): GPT2Attention(\n",
      "          (c_attn): Linear8bitLt(in_features=768, out_features=2304, bias=True)\n",
      "          (c_proj): Linear8bitLt(in_features=768, out_features=768, bias=True)\n",
      "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
      "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
      "        (mlp): GPT2MLP(\n",
      "          (c_fc): Linear8bitLt(in_features=768, out_features=3072, bias=True)\n",
      "          (c_proj): Linear8bitLt(in_features=3072, out_features=768, bias=True)\n",
      "          (act): NewGELUActivation()\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (ln_f): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
      "  )\n",
      "  (score): Linear(in_features=768, out_features=2, bias=False)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "quantization_config = BitsAndBytesConfig(load_in_8bit=True)\n",
    "\n",
    "model = AutoModelForSequenceClassification.from_pretrained(\n",
    "    MODEL_NAME,\n",
    "    quantization_config=quantization_config,\n",
    "    torch_dtype=\"auto\",\n",
    "    num_labels=2,\n",
    "    id2label={0: \"not spam\", 1: \"spam\"},\n",
    "    label2id={\"not spam\": 0, \"spam\": 1},\n",
    "    max_position_embeddings=1024,\n",
    "    use_safetensors=True\n",
    ")\n",
    "\n",
    "# GPT-2 was trained without a pad token, so we align the model config with the tokenizer's pad token.\n",
    "model.config.pad_token_id = tokenizer.eos_token_id\n",
    "print(f\"memory footprint {model.get_memory_footprint()}\")\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea4d9746",
   "metadata": {
    "id": "ea4d9746"
   },
   "source": [
    "# 8. Testing Inference Before Fine-Tuning\n",
    "\n",
    "Let's create a helper function `run_inference` and test it on a spam-like text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "82eebcd5-0cb6-4ca6-a2f9-ca42403b4385",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 356
    },
    "id": "82eebcd5-0cb6-4ca6-a2f9-ca42403b4385",
    "outputId": "ea7fd7ff-75cd-4bcc-91a3-817998c0625a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
      "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text: Two muffins are sitting in an oven. One muffin turns to the other and asks “Is it just me, or is it hot in here?” The other muffin says OH MY GOD A TALKING MUFFIN!!!\n",
      "Predicted label: not spam\n",
      "\n",
      "\n",
      "Text: Amazon is sending you a refunding of $32.64. Please reply with your bank account and routing number to receive your refund.\n",
      "Predicted label: not spam\n"
     ]
    }
   ],
   "source": [
    "def run_inference(text, model):\n",
    "    # Tokenize\n",
    "    encoded_prompt = tokenizer(\n",
    "        text,\n",
    "        truncation=True,\n",
    "        return_tensors=\"pt\"\n",
    "    )\n",
    "    input_ids = encoded_prompt[\"input_ids\"]\n",
    "    attention_mask = encoded_prompt[\"attention_mask\"]\n",
    "\n",
    "    # Run inference\n",
    "    with torch.no_grad():\n",
    "        outputs = model(input_ids, attention_mask=attention_mask)\n",
    "        logits = outputs.logits\n",
    "\n",
    "    # Predict and print\n",
    "    predicted_class_idx = torch.argmax(logits, dim=-1).item()\n",
    "    predicted_label = model.config.id2label[predicted_class_idx]\n",
    "\n",
    "    print(f\"Text: {text}\")\n",
    "    print(f\"Predicted label: {predicted_label}\")\n",
    "\n",
    "text = \"Two muffins are sitting in an oven. One muffin turns to the other and asks “Is it just me, or is it hot in here?” The other muffin says OH MY GOD A TALKING MUFFIN!!!\"\n",
    "run_inference(text, model)\n",
    "print(\"\\n\")\n",
    "text = \"Amazon is sending you a refunding of $32.64. Please reply with your bank account and routing number to receive your refund.\"\n",
    "run_inference(text, model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afv60h9dJfii",
   "metadata": {
    "id": "afv60h9dJfii"
   },
   "source": [
    "# 9. Configure LoRA for GPT-2\n",
    "\n",
    "We define a LoRA configuration, specifying how to adapt GPT-2 using LoRA (Low-Rank Adapters).\n",
    "\n",
    "**rank** and **alpha** are empirically chosen. We’ll start with _r = 8_ and _alpha = 32_ because our model is relatively small and we have a limited training set, making these values a reasonable balance for our needs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d23eac52-2575-46a7-857d-33e4b0831aeb",
   "metadata": {
    "id": "d23eac52-2575-46a7-857d-33e4b0831aeb"
   },
   "outputs": [],
   "source": [
    "lora_config = LoraConfig(\n",
    "    r=8,  # Rank number\n",
    "    lora_alpha=32,  # Scaling factor\n",
    "    task_type=TaskType.SEQ_CLS,  # Sequence Classification Task\n",
    "    fan_in_fan_out=True  # GPT-2 requires this. Info: Source: https://stackoverflow.com/questions/78122986/struggling-with-hugging-face-peft\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cVq7CR9JwoP",
   "metadata": {
    "id": "4cVq7CR9JwoP"
   },
   "source": [
    "# 10. Integrate LoRA with the Base Model\n",
    "\n",
    "We apply the LoRA configuration to our loaded GPT-2 model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "BB7IOVM6JsQw",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BB7IOVM6JsQw",
    "outputId": "228ef347-887b-44ea-90ac-34e3de1098e2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trainable params: 297,984 || all params: 124,737,792 || trainable%: 0.23888830740245906\n"
     ]
    }
   ],
   "source": [
    "peft_model = get_peft_model(model, lora_config)\n",
    "peft_model.print_trainable_parameters()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65955207",
   "metadata": {
    "id": "65955207"
   },
   "source": [
    "# 11. Create Data Collator\n",
    "\n",
    "We create a data collator to pad the tokenized examples to a fixed length for uniformity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "92062902",
   "metadata": {
    "id": "92062902"
   },
   "outputs": [],
   "source": [
    "data_collator = DataCollatorWithPadding(\n",
    "    tokenizer=tokenizer,\n",
    "    padding=\"max_length\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fCd6lXpCTSFc",
   "metadata": {
    "id": "fCd6lXpCTSFc"
   },
   "source": [
    "# 12. Set Up Training Arguments\n",
    "\n",
    "Here we define the training parameters, including batch size, learning rate, and number of epochs.\n",
    "\n",
    "See the [docs](https://huggingface.co/docs/transformers/en/training#training-hyperparameters) for more info."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fKklHCU2XyTK",
   "metadata": {
    "id": "fKklHCU2XyTK"
   },
   "outputs": [],
   "source": [
    "training_args = TrainingArguments(\n",
    "    output_dir=\"peft_model\",\n",
    "    per_device_train_batch_size=2,\n",
    "    per_device_eval_batch_size=2,\n",
    "    evaluation_strategy=\"steps\",\n",
    "    logging_steps=100,\n",
    "    gradient_accumulation_steps=4,\n",
    "    num_train_epochs=1,\n",
    "    weight_decay=0.01,\n",
    "    warmup_steps=50,\n",
    "    lr_scheduler_type=\"cosine\",\n",
    "    learning_rate=5e-4,\n",
    "    save_steps=100,\n",
    "    fp16=True,  # Only to be used with GPU\n",
    "    push_to_hub=False,\n",
    "    report_to=\"none\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cee66f1b",
   "metadata": {
    "id": "cee66f1b"
   },
   "source": [
    "# 13. Define Metrics\n",
    "\n",
    "We define a simple accuracy metric for evaluating our model on the spam classification task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fbe88ca8",
   "metadata": {
    "id": "fbe88ca8"
   },
   "outputs": [],
   "source": [
    "def compute_metrics(eval_pred):\n",
    "    predictions, labels = eval_pred\n",
    "    predictions = np.argmax(predictions, axis=1)\n",
    "    return {\"accuracy\": (predictions == labels).mean()}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "g9KYfBoguF4_",
   "metadata": {
    "id": "g9KYfBoguF4_"
   },
   "source": [
    "# 14. Initialize Trainer\n",
    "\n",
    "Hugging Face's `Trainer` simplifies the training loop. Here we specify:\n",
    "- Our LoRA-adapted model\n",
    "- Training arguments\n",
    "- Tokenizer\n",
    "- Training and evaluation datasets\n",
    "- Data collator\n",
    "- Metrics for evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "tKIjvQxWKTHM",
   "metadata": {
    "id": "tKIjvQxWKTHM"
   },
   "outputs": [],
   "source": [
    "trainer = Trainer(\n",
    "    model=peft_model,\n",
    "    args=training_args,\n",
    "    tokenizer=tokenizer,\n",
    "    train_dataset=tokenized_datasets[\"train\"],\n",
    "    eval_dataset=tokenized_datasets[\"test\"],\n",
    "    data_collator=data_collator,\n",
    "    compute_metrics=compute_metrics,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dda6afff",
   "metadata": {
    "id": "dda6afff"
   },
   "source": [
    "# 15. Train and Evaluate the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ugjw6lUwvT_9",
   "metadata": {
    "id": "ugjw6lUwvT_9",
    "outputId": "cfffb0bd-86c6-4d42-fccc-233777bf3b57"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "You're using a GPT2TokenizerFast 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": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='557' max='557' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [557/557 21:46, Epoch 0/1]\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",
       "      <th>Validation Loss</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>100</td>\n",
       "      <td>0.555500</td>\n",
       "      <td>0.093548</td>\n",
       "      <td>0.982063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>200</td>\n",
       "      <td>0.092900</td>\n",
       "      <td>0.077017</td>\n",
       "      <td>0.983857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>300</td>\n",
       "      <td>0.060500</td>\n",
       "      <td>0.083342</td>\n",
       "      <td>0.985650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>400</td>\n",
       "      <td>0.062000</td>\n",
       "      <td>0.070049</td>\n",
       "      <td>0.985650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>500</td>\n",
       "      <td>0.047400</td>\n",
       "      <td>0.070088</td>\n",
       "      <td>0.986547</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Checkpoint destination directory peft_model/checkpoint-100 already exists and is non-empty.Saving will proceed but saved results may be invalid.\n",
      "/opt/conda/lib/python3.10/site-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
      "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n",
      "Checkpoint destination directory peft_model/checkpoint-200 already exists and is non-empty.Saving will proceed but saved results may be invalid.\n",
      "/opt/conda/lib/python3.10/site-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
      "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n",
      "Checkpoint destination directory peft_model/checkpoint-300 already exists and is non-empty.Saving will proceed but saved results may be invalid.\n",
      "/opt/conda/lib/python3.10/site-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
      "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n",
      "Checkpoint destination directory peft_model/checkpoint-400 already exists and is non-empty.Saving will proceed but saved results may be invalid.\n",
      "/opt/conda/lib/python3.10/site-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
      "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n",
      "Checkpoint destination directory peft_model/checkpoint-500 already exists and is non-empty.Saving will proceed but saved results may be invalid.\n",
      "/opt/conda/lib/python3.10/site-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
      "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='558' max='558' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [558/558 01:28]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'eval_loss': 0.06869951635599136,\n",
       " 'eval_accuracy': 0.9865470852017937,\n",
       " 'eval_runtime': 88.8715,\n",
       " 'eval_samples_per_second': 12.546,\n",
       " 'eval_steps_per_second': 6.279,\n",
       " 'epoch': 1.0}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.train()\n",
    "trainer.evaluate()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b510d45",
   "metadata": {
    "id": "7b510d45"
   },
   "source": [
    "# 16. Save the Model\n",
    "\n",
    "We save our LoRA-adapted GPT-2 model locally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "08a1b982",
   "metadata": {
    "id": "08a1b982"
   },
   "outputs": [],
   "source": [
    "peft_model.save_pretrained(\"peft_model\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6cf07d2",
   "metadata": {
    "id": "e6cf07d2"
   },
   "source": [
    "# 17. Load the Fine-Tuned Model\n",
    "\n",
    "We can reload the fine-tuned model from the saved weights to confirm everything works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "89251079",
   "metadata": {
    "colab": {
     "referenced_widgets": [
      "f35b2129ae4f44b6bc20f99812ecb2e2",
      "9938152fd9b94b9bbacc1fea8beb5e73"
     ]
    },
    "id": "89251079",
    "outputId": "88d3e487-b1c9-466e-ce5a-0d1ba4967fa8"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/student/.local/lib/python3.10/site-packages/huggingface_hub/file_download.py:795: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
      "  warnings.warn(\n",
      "Some weights of GPT2ForSequenceClassification were not initialized from the model checkpoint at gpt2 and are newly initialized: ['score.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PeftModelForSequenceClassification(\n",
      "  (base_model): LoraModel(\n",
      "    (model): GPT2ForSequenceClassification(\n",
      "      (transformer): GPT2Model(\n",
      "        (wte): Embedding(50257, 768)\n",
      "        (wpe): Embedding(1024, 768)\n",
      "        (drop): Dropout(p=0.1, inplace=False)\n",
      "        (h): ModuleList(\n",
      "          (0-11): 12 x GPT2Block(\n",
      "            (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
      "            (attn): GPT2Attention(\n",
      "              (c_attn): Linear(\n",
      "                in_features=768, out_features=2304, bias=True\n",
      "                (lora_dropout): ModuleDict(\n",
      "                  (default): Identity()\n",
      "                )\n",
      "                (lora_A): ModuleDict(\n",
      "                  (default): Linear(in_features=768, out_features=8, bias=False)\n",
      "                )\n",
      "                (lora_B): ModuleDict(\n",
      "                  (default): Linear(in_features=8, out_features=2304, bias=False)\n",
      "                )\n",
      "                (lora_embedding_A): ParameterDict()\n",
      "                (lora_embedding_B): ParameterDict()\n",
      "              )\n",
      "              (c_proj): Conv1D()\n",
      "              (attn_dropout): Dropout(p=0.1, inplace=False)\n",
      "              (resid_dropout): Dropout(p=0.1, inplace=False)\n",
      "            )\n",
      "            (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
      "            (mlp): GPT2MLP(\n",
      "              (c_fc): Conv1D()\n",
      "              (c_proj): Conv1D()\n",
      "              (act): NewGELUActivation()\n",
      "              (dropout): Dropout(p=0.1, inplace=False)\n",
      "            )\n",
      "          )\n",
      "        )\n",
      "        (ln_f): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
      "      )\n",
      "      (score): ModulesToSaveWrapper(\n",
      "        (original_module): Linear(in_features=768, out_features=2, bias=False)\n",
      "        (modules_to_save): ModuleDict(\n",
      "          (default): Linear(in_features=768, out_features=2, bias=False)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "from peft import AutoPeftModelForSequenceClassification\n",
    "peft_model = AutoPeftModelForSequenceClassification.from_pretrained(\"peft_model\")\n",
    "print(peft_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e2300cb",
   "metadata": {
    "id": "0e2300cb"
   },
   "source": [
    "# 18. Test the Final Model on Example Texts\n",
    "\n",
    "We test our saved and reloaded model on both a \"spam\" and \"non-spam\" message."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4de617aa",
   "metadata": {
    "id": "4de617aa",
    "outputId": "4b792220-5304-4ade-a424-1878f9f85f0a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text: Amazon is sending you a refunding of $32.64. Please reply with your bank account and routing number to receive your refund.\n",
      "Predicted label: LABEL_1\n",
      "\n",
      "\n",
      "Text: Two muffins are sitting in an oven. One muffin turns to the other and asks “Is it just me, or is it hot in here?” The other muffin says OH MY GOD A TALKING MUFFIN!!!\n",
      "Predicted label: LABEL_0\n"
     ]
    }
   ],
   "source": [
    "text_spam = \"Amazon is sending you a refunding of $32.64. Please reply with your bank account and routing number to receive your refund.\"\n",
    "text_no_spam = \"Two muffins are sitting in an oven. One muffin turns to the other and asks “Is it just me, or is it hot in here?” The other muffin says OH MY GOD A TALKING MUFFIN!!!\"\n",
    "\n",
    "run_inference(text_spam, peft_model)\n",
    "print(\"\\n\")\n",
    "run_inference(text_no_spam, peft_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c762b976",
   "metadata": {
    "id": "c762b976"
   },
   "source": [
    "# 19. (Optional) Push Model to Hugging Face Hub\n",
    "\n",
    "I think making the model publicly available is good practice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8bf1def1",
   "metadata": {
    "colab": {
     "referenced_widgets": [
      "6729a97bbd82427d9b63178e39803752"
     ]
    },
    "id": "8bf1def1",
    "outputId": "ec22c000-241b-48e0-e404-2031b9f45fc4"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "74f6d9476c124833a0c6e68a64297e4d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "HfHubHTTPError",
     "evalue": "401 Client Error: Unauthorized for url: https://huggingface.co/api/repos/create (Request ID: Root=1-677a9b22-5b17384404e2d12a33c9ac8a;42e1e3ac-32aa-4bd0-8da4-eaaf58a1cbdb)\n\nInvalid username or password.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mHTTPError\u001b[0m                                 Traceback (most recent call last)",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/huggingface_hub/utils/_http.py:406\u001b[0m, in \u001b[0;36mhf_raise_for_status\u001b[0;34m(response, endpoint_name)\u001b[0m\n\u001b[1;32m    405\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 406\u001b[0m     \u001b[43mresponse\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mraise_for_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    407\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m HTTPError \u001b[38;5;28;01mas\u001b[39;00m e:\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/requests/models.py:1024\u001b[0m, in \u001b[0;36mResponse.raise_for_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1023\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m http_error_msg:\n\u001b[0;32m-> 1024\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m HTTPError(http_error_msg, response\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m)\n",
      "\u001b[0;31mHTTPError\u001b[0m: 401 Client Error: Unauthorized for url: https://huggingface.co/api/repos/create",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mHfHubHTTPError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[19], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m login()\n\u001b[0;32m----> 2\u001b[0m \u001b[43mpeft_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpush_to_hub\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mjonathanbenavides/peft-model-gpt2-sms-spam-bitsandbytes-8bits\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/transformers/utils/hub.py:860\u001b[0m, in \u001b[0;36mPushToHubMixin.push_to_hub\u001b[0;34m(self, repo_id, use_temp_dir, commit_message, private, token, max_shard_size, create_pr, safe_serialization, revision, commit_description, **deprecated_kwargs)\u001b[0m\n\u001b[1;32m    857\u001b[0m repo_url \u001b[38;5;241m=\u001b[39m deprecated_kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrepo_url\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m    858\u001b[0m organization \u001b[38;5;241m=\u001b[39m deprecated_kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124morganization\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m--> 860\u001b[0m repo_id \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_create_repo\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    861\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrepo_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprivate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprivate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtoken\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtoken\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrepo_url\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrepo_url\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morganization\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morganization\u001b[49m\n\u001b[1;32m    862\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    864\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m use_temp_dir \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    865\u001b[0m     use_temp_dir \u001b[38;5;241m=\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39misdir(working_dir)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/transformers/utils/hub.py:680\u001b[0m, in \u001b[0;36mPushToHubMixin._create_repo\u001b[0;34m(self, repo_id, private, token, repo_url, organization)\u001b[0m\n\u001b[1;32m    677\u001b[0m             repo_id \u001b[38;5;241m=\u001b[39m repo_id\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/\u001b[39m\u001b[38;5;124m\"\u001b[39m)[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m    678\u001b[0m         repo_id \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00morganization\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrepo_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 680\u001b[0m url \u001b[38;5;241m=\u001b[39m \u001b[43mcreate_repo\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrepo_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrepo_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtoken\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtoken\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprivate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprivate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexist_ok\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m    681\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m url\u001b[38;5;241m.\u001b[39mrepo_id\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py:114\u001b[0m, in \u001b[0;36mvalidate_hf_hub_args.<locals>._inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    111\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m check_use_auth_token:\n\u001b[1;32m    112\u001b[0m     kwargs \u001b[38;5;241m=\u001b[39m smoothly_deprecate_use_auth_token(fn_name\u001b[38;5;241m=\u001b[39mfn\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, has_token\u001b[38;5;241m=\u001b[39mhas_token, kwargs\u001b[38;5;241m=\u001b[39mkwargs)\n\u001b[0;32m--> 114\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/huggingface_hub/hf_api.py:3525\u001b[0m, in \u001b[0;36mHfApi.create_repo\u001b[0;34m(self, repo_id, token, private, repo_type, exist_ok, resource_group_id, space_sdk, space_hardware, space_storage, space_sleep_time, space_secrets, space_variables)\u001b[0m\n\u001b[1;32m   3522\u001b[0m     \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[1;32m   3524\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3525\u001b[0m     \u001b[43mhf_raise_for_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43mr\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3526\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m HTTPError \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m   3527\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m exist_ok \u001b[38;5;129;01mand\u001b[39;00m err\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mstatus_code \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m409\u001b[39m:\n\u001b[1;32m   3528\u001b[0m         \u001b[38;5;66;03m# Repo already exists and `exist_ok=True`\u001b[39;00m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/huggingface_hub/utils/_http.py:477\u001b[0m, in \u001b[0;36mhf_raise_for_status\u001b[0;34m(response, endpoint_name)\u001b[0m\n\u001b[1;32m    473\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m _format(HfHubHTTPError, message, response) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m    475\u001b[0m \u001b[38;5;66;03m# Convert `HTTPError` into a `HfHubHTTPError` to display request information\u001b[39;00m\n\u001b[1;32m    476\u001b[0m \u001b[38;5;66;03m# as well (request id and/or server error message)\u001b[39;00m\n\u001b[0;32m--> 477\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m _format(HfHubHTTPError, \u001b[38;5;28mstr\u001b[39m(e), response) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n",
      "\u001b[0;31mHfHubHTTPError\u001b[0m: 401 Client Error: Unauthorized for url: https://huggingface.co/api/repos/create (Request ID: Root=1-677a9b22-5b17384404e2d12a33c9ac8a;42e1e3ac-32aa-4bd0-8da4-eaaf58a1cbdb)\n\nInvalid username or password."
     ]
    }
   ],
   "source": [
    "login()\n",
    "peft_model.push_to_hub(\"jonathanbenavides/peft-model-gpt2-sms-spam-bitsandbytes-8bits\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "123a44e2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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