4.PEFT Finetune-Bloom-560m-tagger.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WE5GJ6s7y0Xo"
      },
      "source": [
        "# Using 🤗 PEFT & bitsandbytes to finetune a LoRa checkpoint\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "otj46qRbtpnd",
        "outputId": "e85b21e3-2b80-4cb7-97fb-a269dc1847f4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.6/92.6 MB\u001b[0m \u001b[31m9.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m14.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m26.9 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[32m7.8/7.8 MB\u001b[0m \u001b[31m60.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m61.1 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 loralib\n",
        "!pip install -q git+https://github.com/huggingface/transformers.git@main git+https://github.com/huggingface/peft.git"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
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        },
        "id": "DpYr24pR8T_0",
        "outputId": "4fe04ec1-32a7-4410-e6cc-8e25b5b10d46"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a50b635bc5624805b47202ea08b62d1a"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi -L"
      ],
      "metadata": {
        "id": "7650BSUPZh0Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FOtwYRI3zzXI"
      },
      "source": [
        "### Setup the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "cg3fiQOvmI3Q",
        "outputId": "91e39a6d-e1ab-4e78-c91f-c0876189a799"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/715 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5047592295594893a1facfd0bfe95f04"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/1.12G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e5fa56a271ce4fb584eacfd8725482dc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/222 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0d96e48cc22047ae96c49cb1569277a8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading tokenizer.json:   0%|          | 0.00/14.5M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5877358ffe694776a46956064339f928"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/85.0 [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "affd09f22d554678812c7986e01b46a9"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "import os\n",
        "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import bitsandbytes as bnb\n",
        "from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    \"bigscience/bloomz-560m\", #Replace from big model to small model bigscience/bloom-7b1\n",
        "    load_in_8bit=True,\n",
        "    device_map='auto',\n",
        ")\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"bigscience/bloomz-560m\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9fTSZntA1iUG"
      },
      "source": [
        "### Freezing the original weights\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T-gy-LxM0yAi"
      },
      "outputs": [],
      "source": [
        "for param in model.parameters():\n",
        "  param.requires_grad = False  # freeze the model - train adapters later\n",
        "  if param.ndim == 1:\n",
        "    # cast the small parameters (e.g. layernorm) to fp32 for stability\n",
        "    param.data = param.data.to(torch.float32)\n",
        "\n",
        "model.gradient_checkpointing_enable()  # reduce number of stored activations\n",
        "model.enable_input_require_grads()\n",
        "\n",
        "class CastOutputToFloat(nn.Sequential):\n",
        "  def forward(self, x): return super().forward(x).to(torch.float32)\n",
        "model.lm_head = CastOutputToFloat(model.lm_head)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KwOTr7B3NlM3"
      },
      "source": [
        "### Setting up the LoRa Adapters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4iwHGzKBN6wk",
        "outputId": "8d0d7b87-3efa-4794-cf3d-d8b2c581b046"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable params: 1572864 || all params: 560787456 || trainable%: 0.2804741766549072\n"
          ]
        }
      ],
      "source": [
        "from peft import LoraConfig, get_peft_model\n",
        "\n",
        "config = LoraConfig(\n",
        "    r=16, #attention heads\n",
        "    lora_alpha=32, #alpha scaling\n",
        "    # target_modules=[\"q_proj\", \"v_proj\"], #if you know the\n",
        "    lora_dropout=0.05,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\" # set this for CLM or Seq2Seq\n",
        ")\n",
        "\n",
        "model = get_peft_model(model, config)\n",
        "print_trainable_parameters(model)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data"
      ],
      "metadata": {
        "id": "-y6MRpkSfYGP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import transformers\n",
        "from datasets import load_dataset\n",
        "data = load_dataset(\"Abirate/english_quotes\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177,
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            "591611544cbf492a944bb59f90f01578",
            "727f55f1c6fe4ff49c3184cbc8c7e99d",
            "4d9556fded6a429e838d1c63c466698a",
            "6d26949d33bd43ec8b5dfc40a0359b53",
            "91ae2d3421d34c219d41132fd66fa016"
          ]
        },
        "id": "b-En94RAfbKF",
        "outputId": "ef14b3ae-bb0d-4879-e536-8e76c4c76c99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/5.55k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "13febddd742944c19ebf8724a941b6b7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5ff7937a558f45e8aaae77af6ae1d8c5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/647k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "11eedfdd725f4d30a7dfb42e1e02ca99"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "89414086d25d4545837f346250e2b2b4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "997eb346f4064e8da174967e3c01109c"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "id": "7N0lNSarWlPc",
        "outputId": "b6b52cd4-b3f4-4052-8ef2-f2d87d779a58",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['quote', 'author', 'tags'],\n",
              "        num_rows: 2508\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def merge_columns(example):\n",
        "    example[\"prediction\"] = example[\"quote\"] + \" ->: \" + str(example[\"tags\"])\n",
        "    return example\n",
        "\n",
        "data['train'] = data['train'].map(merge_columns)\n",
        "data['train'][\"prediction\"][:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 171,
          "referenced_widgets": [
            "a7538fcaad3e40b2a42a09d0dba06e49",
            "639610cfd8ce470086a0c6401e30090d",
            "1e274afa734f4608bdc8337672cee90e",
            "6ff6ccdd13ab4e0684fea9a859882497",
            "d019e817bf764097b8f828eafaa74e83",
            "b7574ecb07c7468c8e3f66e81825dd1c",
            "0af12121ce254291a386b663b79b1de1",
            "330b0307269c4c18abf7b1fff6531c74",
            "f1ea33a9f9a3414caed978742dcf7577",
            "c1ddbc96d96141f4b1097c083e960aa0",
            "ba5fe45fa4164a1289d1082ee69f790d"
          ]
        },
        "id": "b6fQzRl2faSn",
        "outputId": "7d6d8c70-45f1-4ae3-e11e-b0708550b00e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/2508 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a7538fcaad3e40b2a42a09d0dba06e49"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[\"“Be yourself; everyone else is already taken.” ->: ['be-yourself', 'gilbert-perreira', 'honesty', 'inspirational', 'misattributed-oscar-wilde', 'quote-investigator']\",\n",
              " \"“I'm selfish, impatient and a little insecure. I make mistakes, I am out of control and at times hard to handle. But if you can't handle me at my worst, then you sure as hell don't deserve me at my best.” ->: ['best', 'life', 'love', 'mistakes', 'out-of-control', 'truth', 'worst']\",\n",
              " \"“Two things are infinite: the universe and human stupidity; and I'm not sure about the universe.” ->: ['human-nature', 'humor', 'infinity', 'philosophy', 'science', 'stupidity', 'universe']\",\n",
              " \"“So many books, so little time.” ->: ['books', 'humor']\",\n",
              " \"“A room without books is like a body without a soul.” ->: ['books', 'simile', 'soul']\"]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['train'][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jL17IZCFhqw-",
        "outputId": "397f1f8a-aee5-4852-e88a-1a29f7289da3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'quote': '“Be yourself; everyone else is already taken.”',\n",
              " 'author': 'Oscar Wilde',\n",
              " 'tags': ['be-yourself',\n",
              "  'gilbert-perreira',\n",
              "  'honesty',\n",
              "  'inspirational',\n",
              "  'misattributed-oscar-wilde',\n",
              "  'quote-investigator'],\n",
              " 'prediction': \"“Be yourself; everyone else is already taken.” ->: ['be-yourself', 'gilbert-perreira', 'honesty', 'inspirational', 'misattributed-oscar-wilde', 'quote-investigator']\"}"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = data.map(lambda samples: tokenizer(samples['prediction']), batched=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "4c478b34600e4dac837182b33aa1a3ba",
            "4545724b92c143608d122daf1b9e484d",
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            "d2578e8746ad4534a9d11bb87e5d497e",
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            "3f94f6884e904f259e462fff5b2be43e",
            "8a9d4bcb29664e0aa887b9df0144448f",
            "d47239d7c1204e4a8fbb1ce739681c69"
          ]
        },
        "id": "v2huC6dMh5vE",
        "outputId": "0a212a04-3276-4321-ffd1-fb2fe53145d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/2508 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4c478b34600e4dac837182b33aa1a3ba"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dNIP6Z8Wh-W3",
        "outputId": "5bf73ad8-0b5f-4db3-f263-2ef746332de4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['quote', 'author', 'tags', 'prediction', 'input_ids', 'attention_mask'],\n",
              "        num_rows: 2508\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QdjWif4CVXR6"
      },
      "source": [
        "### Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AQ_HCYruWIHU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "outputId": "f1e80b78-8baa-4f9b-deff-5c052026fb45"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='200' max='200' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [200/200 07:14, Epoch 1/2]\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>25</td>\n",
              "      <td>2.737700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>2.697300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>75</td>\n",
              "      <td>2.713300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>2.690200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>125</td>\n",
              "      <td>2.679900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>150</td>\n",
              "      <td>2.733400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>175</td>\n",
              "      <td>2.585700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>2.693500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=200, training_loss=2.6913645172119143, metrics={'train_runtime': 437.0945, 'train_samples_per_second': 7.321, 'train_steps_per_second': 0.458, 'total_flos': 663257454772224.0, 'train_loss': 2.6913645172119143, 'epoch': 1.28})"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "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=25,\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": 118,
          "referenced_widgets": [
            "555c91aa0cec4f8b87e6d2381d451ced",
            "455005125a97474185a790111558109a",
            "42a15cb6853447b9ac9b0ff0e4edb3b9",
            "4499326712fa49c7a9aeebf28c491e74",
            "1cc1e5ab4a7c4131b586e1084332ca05",
            "fbaf803a8a264a21a713668fee49d7ca",
            "261c7698360b4ce3a66b8bf38713bb37",
            "4222b0ff032348f0b1476d6af7904fe5",
            "dba9fb4b5d2646b09f2f6b3b7a6c9cdf",
            "6c959620cb464c369028c6a969f9ecde",
            "119e8773331645bf82b98e3f227fe87c"
          ]
        },
        "id": "VxB6UV5XAvvP",
        "outputId": "7d336948-0640-47c6-cf4b-c1bcc980b398"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py:844: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "adapter_model.bin:   0%|          | 0.00/6.31M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "555c91aa0cec4f8b87e6d2381d451ced"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/ashishpatel26/bloomz-560m-tagger/commit/8f3e3635208bd792396c44fd06307ee6b69ff08e', commit_message='basic training', commit_description='', oid='8f3e3635208bd792396c44fd06307ee6b69ff08e', pr_url=None, pr_revision=None, pr_num=None)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "model.push_to_hub(\"ashishpatel26/bloomz-560m-tagger\",\n",
        "                  use_auth_token=True,\n",
        "                  commit_message=\"basic training\",\n",
        "                  private=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S65GcxNGA9kz"
      },
      "source": [
        "## Load adapters from the Hub"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "d467bfd805034e5ba91f9db956de28f8",
            "08a2e6ad467d4d85b89f8177773989eb",
            "70f54d0fb63b4d3b933eae7366af774a",
            "e1912e16cb554a51927ba6831af4b05a",
            "297f8725628148a18d149e28290c6d3a",
            "3c5d3c7b447544519318b7dd79ec9397",
            "fb5014c40e894deb957a305f882e353f",
            "1070b30f7d0a4151876b1304eb98ceb1",
            "23b3f0a52a7a48af8ec3d6b72bc1693c",
            "0cc9bfbdec5a4c6d857e824ecbddc47f",
            "74efb99a46da430ea7541873f84073b3",
            "f8f4451e84b8411e8d94d6e199e0be6f",
            "eed293148ab54465b1cdb2517be43280",
            "59e2ea1c4424436bbe79de388c1b0485",
            "8472413159ed49c3b9560151495295f0",
            "1359e859faa9486aacf6ba499fb9c963",
            "4edacc618c8545c69dfeb690d723ab9b",
            "85cb2f2d05aa4179b61d072cb785125b",
            "010e276575484ff486f6cfaa7b2e3256",
            "45d1570deb9c44f1a64136ed62181aa6",
            "239caa73bf704f8b8b15cea2f604516a",
            "66653758626b4b92a7640e494adf4b21"
          ]
        },
        "id": "hsD1VKqeA62Z",
        "outputId": "cad10e5b-984f-4035-97eb-265fe03e6851"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/adapter_config.json:   0%|          | 0.00/440 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d467bfd805034e5ba91f9db956de28f8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading adapter_model.bin:   0%|          | 0.00/6.31M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f8f4451e84b8411e8d94d6e199e0be6f"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "import torch\n",
        "from peft import PeftModel, PeftConfig\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
        "\n",
        "peft_model_id = \"ashishpatel26/bloomz-560m-tagger\"\n",
        "config = PeftConfig.from_pretrained(peft_model_id)\n",
        "model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=True, device_map='auto')\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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MDqJWba-tpnv",
        "outputId": "e7acfe5a-4da3-4628-9357-b3aba4237b36"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1529: 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"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            " “Training models with PEFT and LoRa is cool” ->:  ['training'], 'trainingmodels', 'trainingmodels-cloud'], 'trainingmodels', 'trainingmodels-cloud'], 'trainingmodels-cloud'], 'trainingmodels-cloud'], '\n"
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