examples/int8_training/Finetune_opt_bnb_peft.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WE5GJ6s7y0Xo"
   },
   "source": [
    "## Fine-tune large models using 🤗 `peft` adapters, `transformers` & `bitsandbytes`\n",
    "\n",
    "In this tutorial we will cover how we can fine-tune large language models using the very recent `peft` library and `bitsandbytes` for loading large models in 8-bit.\n",
    "The fine-tuning method will rely on a recent method called \"Low Rank Adapters\" (LoRA), instead of fine-tuning the entire model you just have to fine-tune these adapters and load them properly inside the model. \n",
    "After fine-tuning the model you can also share your adapters on the 🤗 Hub and load them very easily. Let's get started!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TfBzP8gWzkpv"
   },
   "source": [
    "### Install requirements\n",
    "\n",
    "First, run the cells below to install the requirements:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "otj46qRbtpnd",
    "outputId": "2aa109f6-3f4e-4887-a16e-336f51e7cc9a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.3/76.3 MB\u001b[0m \u001b[31m10.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m462.8/462.8 KB\u001b[0m \u001b[31m25.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.7/199.7 KB\u001b[0m \u001b[31m25.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m190.3/190.3 KB\u001b[0m \u001b[31m23.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m213.0/213.0 KB\u001b[0m \u001b[31m26.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m132.0/132.0 KB\u001b[0m \u001b[31m18.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m140.6/140.6 KB\u001b[0m \u001b[31m20.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
      "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
      "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
      "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
      "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
      "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m72.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h  Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
      "  Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
     ]
    }
   ],
   "source": [
    "!pip install -q bitsandbytes datasets accelerate\n",
    "!pip install -q git+https://github.com/huggingface/transformers.git@main git+https://github.com/huggingface/peft.git"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FOtwYRI3zzXI"
   },
   "source": [
    "### Model loading\n",
    "\n",
    "Here let's load the `opt-6.7b` model, its weights in half-precision (float16) are about 13GB on the Hub! If we load them in 8-bit we would require around 7GB of memory instead."
   ]
  },
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    },
    "id": "cg3fiQOvmI3Q",
    "outputId": "135a7675-6a4d-4786-b5dc-34cb867f40c7"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bee2f575b3e64c30b2f3afa137802406",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import bitsandbytes as bnb\n",
    "from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM, BitsAndBytesConfig\n",
    "\n",
    "model = AutoModelForCausalLM.from_pretrained(\"facebook/opt-6.7b\", quantization_config=BitsAndBytesConfig(load_in_8bit=True))\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"facebook/opt-6.7b\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "9fTSZntA1iUG"
   },
   "source": [
    "### Prepare model for training\n",
    "\n",
    "Some pre-processing needs to be done before training such an int8 model using `peft`, therefore let's import an utiliy function `prepare_model_for_kbit_training` that will: \n",
    "- Casts all the non `int8` modules to full precision (`fp32`) for stability\n",
    "- Add a `forward_hook` to the input embedding layer to enable gradient computation of the input hidden states\n",
    "- Enable gradient checkpointing for more memory-efficient training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "T-gy-LxM0yAi"
   },
   "outputs": [],
   "source": [
    "from peft import prepare_model_for_kbit_training\n",
    "\n",
    "model = prepare_model_for_kbit_training(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KwOTr7B3NlM3"
   },
   "source": [
    "### Apply LoRA\n",
    "\n",
    "Here comes the magic with `peft`! Let's load a `PeftModel` and specify that we are going to use low-rank adapters (LoRA) using `get_peft_model` utility function from `peft`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "4W1j6lxaNnxC"
   },
   "outputs": [],
   "source": [
    "def print_trainable_parameters(model):\n",
    "    \"\"\"\n",
    "    Prints the number of trainable parameters in the model.\n",
    "    \"\"\"\n",
    "    trainable_params = 0\n",
    "    all_param = 0\n",
    "    for _, param in model.named_parameters():\n",
    "        all_param += param.numel()\n",
    "        if param.requires_grad:\n",
    "            trainable_params += param.numel()\n",
    "    print(\n",
    "        f\"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4iwHGzKBN6wk",
    "outputId": "039f7175-14c9-42b4-a078-80d27aab161c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trainable params: 8388608 || all params: 6666862592 || trainable%: 0.12582542214183376\n"
     ]
    }
   ],
   "source": [
    "from peft import LoraConfig, get_peft_model\n",
    "\n",
    "config = LoraConfig(\n",
    "    r=16, lora_alpha=32, target_modules=[\"q_proj\", \"v_proj\"], lora_dropout=0.05, bias=\"none\", task_type=\"CAUSAL_LM\"\n",
    ")\n",
    "\n",
    "model = get_peft_model(model, config)\n",
    "print_trainable_parameters(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QdjWif4CVXR6"
   },
   "source": [
    "### Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "b_RPiO7f3ClX"
   },
   "source": []
  },
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      "c9b718882fec4254bce1f33fa9373921",
      "f27905d0073e493cb9dcd174c0f15e35",
      "0f634721a70d4e749cb616e55ab747b3",
      "e1f62cbd805d4b8aa9aba7e345c21c82",
      "893d8cb7857147ba82ac86d140f69a5c",
      "4884bf82f5814e049c47cf6d496aab08",
      "432fc8277ebc492f91d6b46ed073ccb4",
      "c423a3cc0b504828b11077c77268ed92",
      "668dd47eec9942bcad0af209772cf8e6",
      "a618f430e06645eb9c95bf16bb6ea59a",
      "bba20d9bf1974a7f8d1ebcb9f5c4cd49",
      "5cec06ccf9074e019ea1f7daf17a0319",
      "faf24b3ed994422f8dd806ae0cc30531",
      "79f7929f423d493caf86b548e91e8a42",
      "23cb4d44c4534bae80357c8a9f6b86ba",
      "4fa82dbe7d3a4b3996b1fa1cf9c23498",
      "6eee224b244a4cafb3fc0f3b16eb0d03",
      "73344836205f4569a4113c24985bd5b1",
      "86dff0987bf040da99c8f2846da26d86",
      "63f8ad255d2147128bdc26f47fdf2528"
     ]
    },
    "id": "AQ_HCYruWIHU",
    "outputId": "e9baadaf-e202-413d-87f0-74c730d78408"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f357166c6e5f43f39d0a287ca6d6f60e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading readme:   0%|          | 0.00/5.55k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:datasets.builder:Using custom data configuration Abirate--english_quotes-6e72855d06356857\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset json/Abirate--english_quotes to /root/.cache/huggingface/datasets/Abirate___json/Abirate--english_quotes-6e72855d06356857/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c81d20fe47ce4b7594427830d71504d7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ae58b3f129644729aa2f890b1712fc6e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data:   0%|          | 0.00/647k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b8c29c1accf4479da91d393dd59ca82b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "041944011c204558a4315c27ec5dabce",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset json downloaded and prepared to /root/.cache/huggingface/datasets/Abirate___json/Abirate--english_quotes-6e72855d06356857/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51. Subsequent calls will reuse this data.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9225dd50bcea435289fa2eba64087076",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a618f430e06645eb9c95bf16bb6ea59a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The model is loaded in 8-bit precision. To train this model you need to add additional modules inside the model such as adapters using `peft` library and freeze the model weights. Please check  the examples in https://github.com/huggingface/peft for more details.\n",
      "max_steps is given, it will override any value given in num_train_epochs\n",
      "Using cuda_amp half precision backend\n",
      "The following columns in the training set don't have a corresponding argument in `PeftModelForCausalLM.forward` and have been ignored: author, tags, quote. If author, tags, quote are not expected by `PeftModelForCausalLM.forward`,  you can safely ignore this message.\n",
      "/usr/local/lib/python3.8/dist-packages/transformers/optimization.py:306: 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",
      "***** Running training *****\n",
      "  Num examples = 2508\n",
      "  Num Epochs = 2\n",
      "  Instantaneous batch size per device = 4\n",
      "  Total train batch size (w. parallel, distributed & accumulation) = 16\n",
      "  Gradient Accumulation steps = 4\n",
      "  Total optimization steps = 200\n",
      "  Number of trainable parameters = 8388608\n",
      "/usr/local/lib/python3.8/dist-packages/bitsandbytes/autograd/_functions.py:298: 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='153' max='200' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [153/200 26:04 < 08:06, 0.10 it/s, Epoch 0.97/2]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2.364400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2.200400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2.302300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2.184700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.878700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>2.307200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>2.193800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>2.446200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>2.458900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>2.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>1.941200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>1.931000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>2.055900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1.975100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>2.015100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>2.095600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1.768300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>2.155700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>2.402300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>2.124600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>2.314900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>1.908500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>2.078800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>1.941900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>1.879800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>1.927500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>1.371400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>1.977600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>2.055000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>1.915800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>1.958100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>2.195900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>2.001000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>2.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>1.576900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>1.879800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>1.821600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>1.727800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>1.995700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>1.698600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>2.129300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>2.025800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>1.696500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>1.984700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>2.051100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>2.054400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>1.765600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>2.063100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>1.746900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>1.873000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>2.391300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>2.494100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>2.072300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>1.808000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>1.911900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>2.168100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>2.166100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>1.921500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>1.856000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>1.652800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>1.605000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>2.032500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>1.822100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>1.623600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>1.923200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>2.053200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>2.114300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>1.807700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>1.857800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>1.854600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>2.023000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>1.864900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>1.769300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>1.837700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>1.742200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>1.895900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>1.922800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>2.325300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>2.231200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>2.309500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>1.945700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>2.072100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>1.917400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>2.004600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>85</td>\n",
       "      <td>1.951700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>86</td>\n",
       "      <td>1.450600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>1.785600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88</td>\n",
       "      <td>1.668000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>1.903100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>1.709800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>2.312900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>92</td>\n",
       "      <td>2.092100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>2.319600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>1.603100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>1.740000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>1.670500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>1.611600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>1.728900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>2.285200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>100</td>\n",
       "      <td>1.957800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>101</td>\n",
       "      <td>1.676700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>102</td>\n",
       "      <td>1.656300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>103</td>\n",
       "      <td>1.612400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>104</td>\n",
       "      <td>1.848900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>105</td>\n",
       "      <td>1.870000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>106</td>\n",
       "      <td>1.954000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>107</td>\n",
       "      <td>2.192200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>108</td>\n",
       "      <td>1.637600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>109</td>\n",
       "      <td>1.208700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>110</td>\n",
       "      <td>2.254200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>111</td>\n",
       "      <td>1.832100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>112</td>\n",
       "      <td>2.119600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>113</td>\n",
       "      <td>2.126400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>114</td>\n",
       "      <td>1.915700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>115</td>\n",
       "      <td>1.587500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>116</td>\n",
       "      <td>1.564800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>117</td>\n",
       "      <td>1.742700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>118</td>\n",
       "      <td>1.712600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>119</td>\n",
       "      <td>1.727900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>120</td>\n",
       "      <td>2.361500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>121</td>\n",
       "      <td>2.070300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>122</td>\n",
       "      <td>1.878500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>123</td>\n",
       "      <td>1.846600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>124</td>\n",
       "      <td>2.061700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>125</td>\n",
       "      <td>2.149700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>126</td>\n",
       "      <td>1.940600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>127</td>\n",
       "      <td>2.098300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>128</td>\n",
       "      <td>1.734100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>129</td>\n",
       "      <td>2.111700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>130</td>\n",
       "      <td>1.887600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>131</td>\n",
       "      <td>1.716300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>132</td>\n",
       "      <td>2.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>133</td>\n",
       "      <td>1.782200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>134</td>\n",
       "      <td>1.955200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>135</td>\n",
       "      <td>1.762900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>136</td>\n",
       "      <td>1.954700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>137</td>\n",
       "      <td>1.687100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>138</td>\n",
       "      <td>1.979100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>139</td>\n",
       "      <td>1.634600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>140</td>\n",
       "      <td>1.801200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>141</td>\n",
       "      <td>1.954100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>142</td>\n",
       "      <td>1.833900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>143</td>\n",
       "      <td>2.051400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>144</td>\n",
       "      <td>1.921200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>145</td>\n",
       "      <td>1.787500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>146</td>\n",
       "      <td>1.825400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>147</td>\n",
       "      <td>1.363400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>148</td>\n",
       "      <td>1.977400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>149</td>\n",
       "      <td>1.768300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>150</td>\n",
       "      <td>2.226700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>151</td>\n",
       "      <td>1.945500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import transformers\n",
    "from datasets import load_dataset\n",
    "\n",
    "data = load_dataset(\"Abirate/english_quotes\")\n",
    "data = data.map(lambda samples: tokenizer(samples[\"quote\"]), batched=True)\n",
    "\n",
    "trainer = transformers.Trainer(\n",
    "    model=model,\n",
    "    train_dataset=data[\"train\"],\n",
    "    args=transformers.TrainingArguments(\n",
    "        per_device_train_batch_size=4,\n",
    "        gradient_accumulation_steps=4,\n",
    "        warmup_steps=100,\n",
    "        max_steps=200,\n",
    "        learning_rate=2e-4,\n",
    "        fp16=True,\n",
    "        logging_steps=1,\n",
    "        output_dir=\"outputs\",\n",
    "    ),\n",
    "    data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),\n",
    ")\n",
    "model.config.use_cache = False  # silence the warnings. Please re-enable for inference!\n",
    "trainer.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Duak7T_B3VpJ"
   },
   "source": [
    "## Share adapters on the 🤗 Hub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 331,
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      "4eccb670e98043b3b2702821a3060ece",
      "a333501a50df4b9fa9546d8d965e0dc3",
      "1f173cb95c5c44f4b32f6cfe10ee3b03",
      "52c8a7e673f24276a07042388a13b58f",
      "a0f323ccfbc14fc4b7a5e7046b221ce3"
     ]
    },
    "id": "DpYr24pR8T_0",
    "outputId": "20186456-1bd4-4655-b2f2-8f24f9f37fcc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Token is valid.\n",
      "Your token has been saved to /root/.cache/huggingface/token\n",
      "Login successful\n"
     ]
    }
   ],
   "source": [
    "from huggingface_hub import notebook_login\n",
    "\n",
    "notebook_login()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 133,
     "referenced_widgets": [
      "3dbe077ed0c34e4eb1628418138ccbc6",
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      "549bd12e3af64256a7534903688835a8",
      "a18d9beb34b848ff8cc541d2cb290c4c",
      "876cb42184f54b749c442163290c2c45",
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      "7164feaf360d4300a5083f95064b144a",
      "bd7547126a874a45b608afed7ab2958b",
      "26e4b6b94e4540728c59df8e481ce43d",
      "9d1b77500f1c45308d4791a9c443e307",
      "b2693135b6954d35afb3120f3caf4000",
      "aeca2429ee48450a814515cb06acbc3e",
      "434c09950be04d728cd7ca8d6c134dc6",
      "34f58c132d2e4249b1e62a0b57c85999",
      "d148369be26a43949257790cb202728d",
      "0ecf58c5cbcd40908595fccddff1c6d4",
      "fc2314656a2745eb921f636cc3451381",
      "7817f8669b7f449fadf02d7145fa89e2",
      "06e012eea9714ed589344a362b7421bb",
      "504e9e5ced0348cc87aafae0c1c372eb",
      "5b538e8389fb4574a5dfdc554624e3c8"
     ]
    },
    "id": "VxB6UV5XAvvP",
    "outputId": "c3b0133b-f5b1-4283-8367-f06524bea46c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Uploading the following files to ybelkada/opt-6.7b-lora: adapter_config.json,adapter_model.bin\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3dbe077ed0c34e4eb1628418138ccbc6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Upload 1 LFS files:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b2693135b6954d35afb3120f3caf4000",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "adapter_model.bin:   0%|          | 0.00/33.6M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "CommitInfo(commit_url='https://huggingface.co/ybelkada/opt-6.7b-lora/commit/6f240b184e666b54a51b3fe482e4711448e6c751', commit_message='Upload model', commit_description='', oid='6f240b184e666b54a51b3fe482e4711448e6c751', pr_url=None, pr_revision=None, pr_num=None)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.push_to_hub(\"ybelkada/opt-6.7b-lora\", use_auth_token=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "S65GcxNGA9kz"
   },
   "source": [
    "## Load adapters from the Hub\n",
    "\n",
    "You can also directly load adapters from the Hub using the commands below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 517,
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      "1e94c570880449d0a2763bc1eba09057",
      "d72bd17e161442b0979dceaaef66d82c",
      "2396a162f70d41a384afd1fcb2f78d11",
      "39f1a52884e3440c8b521e493d6e1a53",
      "528b93d088054955906282b1c9f93a17",
      "99b9d36317a34143acf982fa3b089fda",
      "4f592d7632fd440aac0bf97ceed2de75",
      "1b515483e5884479b2101127c16321d4",
      "ce8d4bac782949579a2e52864455d9de",
      "523deba849044669bfe8c959750708fc"
     ]
    },
    "id": "hsD1VKqeA62Z",
    "outputId": "83ecbf31-3f84-4b8e-d667-10317f7adcec"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "621d427f78fb458e8ae25262f2ab7ca8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)/adapter_config.json:   0%|          | 0.00/332 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4a2107423a164efd89002e031126c8b5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "43f2a9b0f37e4caab35d7dda43f051f9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading adapter_model.bin:   0%|          | 0.00/33.6M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "from peft import PeftModel, PeftConfig\n",
    "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig\n",
    "\n",
    "peft_model_id = \"ybelkada/opt-6.7b-lora\"\n",
    "config = PeftConfig.from_pretrained(peft_model_id)\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    config.base_model_name_or_path, return_dict=True, quantization_config=BitsAndBytesConfig(load_in_8bit=True), device_map=\"auto\"\n",
    ")\n",
    "tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n",
    "\n",
    "# Load the Lora model\n",
    "model = PeftModel.from_pretrained(model, peft_model_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MHYljmTjj5wX"
   },
   "source": [
    "## Inference\n",
    "\n",
    "You can then directly use the trained model or the model that you have loaded from the 🤗 Hub for inference as you would do it usually in `transformers`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MDqJWba-tpnv",
    "outputId": "dd65644f-6921-46e1-920c-400bd7d888cf"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/marc/anaconda3/envs/accelerate/lib/python3.10/site-packages/transformers/generation/utils.py:1448: UserWarning: You are calling .generate() with the `input_ids` being on a device type different than your model's device. `input_ids` is on cpu, whereas the model is on cuda. You may experience unexpected behaviors or slower generation. Please make sure that you have put `input_ids` to the correct device by calling for example input_ids = input_ids.to('cuda') before running `.generate()`.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      " Two things are infinite:  the universe and human stupidity; and I'm not sure about the universe.  -Albert Einstein\n",
      "I'm not sure about the universe either.\n"
     ]
    }
   ],
   "source": [
    "batch = tokenizer(\"Two things are infinite: \", return_tensors=\"pt\")\n",
    "\n",
    "with torch.cuda.amp.autocast():\n",
    "    output_tokens = model.generate(**batch, max_new_tokens=50)\n",
    "\n",
    "print(\"\\n\\n\", tokenizer.decode(output_tokens[0], skip_special_tokens=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HZL8ZbcJCHoy"
   },
   "source": [
    "As you can see by fine-tuning for few steps we have almost recovered the quote from Albert Einstein that is present in the [training data](https://huggingface.co/datasets/Abirate/english_quotes)."
   ]
  }
 ],
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