diff --git a/examples/int8_training/PEFT_FlanT5_large_int8.ipynb b/examples/int8_training/PEFT_FlanT5_large_int8.ipynb
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@@ -0,0 +1,8351 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Fine-tune FLAN-T5 using `bitsandbytes`, `peft` & `transformers` 🤗 "
+      ],
+      "metadata": {
+        "id": "lw1cWgq-DI5k"
+      },
+      "id": "lw1cWgq-DI5k"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "In this notebook we will see how to properly use `peft` , `transformers` & `bitsandbytes` to fine-tune `flan-t5-large` in a google colab!"
+      ],
+      "metadata": {
+        "id": "kBFPA3-aDT7H"
+      },
+      "id": "kBFPA3-aDT7H"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Install requirements"
+      ],
+      "metadata": {
+        "id": "ShAuuHCDDkvk"
+      },
+      "id": "ShAuuHCDDkvk"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install -q bitsandbytes datasets accelerate loralib\n",
+        "!pip install -q git+https://github.com/huggingface/transformers.git@main git+https://github.com/younesbelkada/peft.git@int8-wrapper"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "DRQ4ZrJTDkSy",
+        "outputId": "31b108ee-a34c-4a1f-a970-6fa1809b64c5"
+      },
+      "id": "DRQ4ZrJTDkSy",
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.3/76.3 MB\u001b[0m \u001b[31m10.6 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[31m45.6 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[31m26.9 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[31m20.1 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[31m26.8 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[31m26.5 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[31m65.2 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"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Import model and tokenizer"
+      ],
+      "metadata": {
+        "id": "QBdCIrizDxFw"
+      },
+      "id": "QBdCIrizDxFw"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "dd3c5acc",
+      "metadata": {
+        "id": "dd3c5acc",
+        "colab": {
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+          "height": 350,
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+          ]
+        },
+        "outputId": "fab398b4-dace-4f44-89e5-687ec74b18eb"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading (…)lve/main/config.json:   0%|          | 0.00/662 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "59782bac41834abbbd6cbd6614c2e57b"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Overriding torch_dtype=None with `torch_dtype=torch.float16` due to requirements of `bitsandbytes` to enable model loading in mixed int8. Either pass torch_dtype=torch.float16 or don't pass this argument at all to remove this warning.\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading (…)\"pytorch_model.bin\";:   0%|          | 0.00/3.13G [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "dbdb787728184aa1a6906f96c5e6f929"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "===================================BUG REPORT===================================\n",
+            "Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
+            "================================================================================\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading (…)neration_config.json:   0%|          | 0.00/147 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "aa2c51ad05c14a02a13e5c047779fc05"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading (…)okenizer_config.json:   0%|          | 0.00/2.54k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "1b839f5b472e44148c9e02a12550fade"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading (…)\"spiece.model\";:   0%|          | 0.00/792k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "65aacfc888014bdea55253428b2569c0"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/2.42M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "d7d9e2e2090d4226ad89e5ba9cec33df"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/2.20k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "0363c4d4dc1449148b09061763ea119a"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Select CUDA device index\n",
+        "import os\n",
+        "import torch\n",
+        "\n",
+        "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
+        "\n",
+        "from datasets import load_dataset\n",
+        "from transformers import AutoModelForSeq2SeqLM, AutoTokenizer\n",
+        "\n",
+        "model_name = \"google/flan-t5-large\"\n",
+        "\n",
+        "model = AutoModelForSeq2SeqLM.from_pretrained(\n",
+        "    model_name, \n",
+        "    load_in_8bit=True, \n",
+        "    device_map='auto'\n",
+        ")\n",
+        "tokenizer = AutoTokenizer.from_pretrained(model_name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Prepare model for training"
+      ],
+      "metadata": {
+        "id": "VwcHieQzD_dl"
+      },
+      "id": "VwcHieQzD_dl"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Some pre-processing needs to be done before training such a model using `peft`, therefore let's import an utiliy function `prepare_model_for_training`"
+      ],
+      "metadata": {
+        "id": "4o3ePxrjEDzv"
+      },
+      "id": "4o3ePxrjEDzv"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "1629ebcb",
+      "metadata": {
+        "id": "1629ebcb"
+      },
+      "outputs": [],
+      "source": [
+        "from peft import prepare_model_for_training\n",
+        "\n",
+        "model = prepare_model_for_training(model)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Load your `PeftModel` \n",
+        "\n",
+        "Here we will use LoRA (Low-Rank Adaptators) to train our model"
+      ],
+      "metadata": {
+        "id": "iCpAgawAEieu"
+      },
+      "id": "iCpAgawAEieu"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "17566ae3",
+      "metadata": {
+        "id": "17566ae3",
+        "outputId": "66cbd0f3-815d-4d68-c0a3-6b2e5f46b021",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "trainable params: 4718592 || all params: 787868672 || trainable%: 0.5989059049678777\n"
+          ]
+        }
+      ],
+      "source": [
+        "from peft import LoraConfig, get_peft_model,TaskType\n",
+        "\n",
+        "\n",
+        "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",
+        "    )\n",
+        "\n",
+        "lora_config = LoraConfig(\n",
+        "    r=16,\n",
+        "    lora_alpha=32,\n",
+        "    target_modules=[\"q\", \"v\"],\n",
+        "    lora_dropout=0.05,\n",
+        "    bias=\"none\",\n",
+        "    task_type=\"SEQ_2_SEQ_LM\"\n",
+        ")\n",
+        "\n",
+        "\n",
+        "model = get_peft_model(model, lora_config)\n",
+        "print_trainable_parameters(model)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Load and process data\n",
+        "\n",
+        "Here we will use [`financial_phrasebank`](https://huggingface.co/datasets/financial_phrasebank) dataset to fine-tune our model on sentiment classification on financial sentences."
+      ],
+      "metadata": {
+        "id": "HsG0x6Z7FwjZ"
+      },
+      "id": "HsG0x6Z7FwjZ"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "242cdfae",
+      "metadata": {
+        "id": "242cdfae",
+        "outputId": "3122a25f-4299-4c3e-cd93-3a507f9f0d91",
+        "colab": {
+          "referenced_widgets": [
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+            "6f30253108fb4dce9c3de029457ef6f1",
+            "1fa2a7e3ff3c4c99ab95e96a28624846"
+          ],
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading builder script:   0%|          | 0.00/6.04k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "ce0213e9d6aa45c5a9ac9954fbe15f62"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading metadata:   0%|          | 0.00/13.7k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "f2dc5e8a31c348358aca916274899e8b"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading readme:   0%|          | 0.00/8.86k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "94437f56e5a44fa3bb08c9d798b2eaeb"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Downloading and preparing dataset financial_phrasebank/sentences_allagree to /root/.cache/huggingface/datasets/financial_phrasebank/sentences_allagree/1.0.0/550bde12e6c30e2674da973a55f57edde5181d53f5a5a34c1531c53f93b7e141...\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading data:   0%|          | 0.00/682k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "cce112d791dd4b748908756e785ab555"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating train split:   0%|          | 0/2264 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "41d8dbe04d0c4ec4a9d19662f9e11920"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Dataset financial_phrasebank downloaded and prepared to /root/.cache/huggingface/datasets/financial_phrasebank/sentences_allagree/1.0.0/550bde12e6c30e2674da973a55f57edde5181d53f5a5a34c1531c53f93b7e141. Subsequent calls will reuse this data.\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  0%|          | 0/1 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "26e1bfb23d3a4797a8dbb9c4ed2aeb22"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  0%|          | 0/3 [00:00<?, ?ba/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "283bdd8a4daf4f5ca1ec6e9ed19c46ec"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  0%|          | 0/1 [00:00<?, ?ba/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "8d35a041dbfb4747aea427e76890551a"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# loading dataset\n",
+        "dataset = load_dataset(\"financial_phrasebank\", 'sentences_allagree')\n",
+        "dataset = dataset[\"train\"].train_test_split(test_size=0.1)\n",
+        "dataset[\"validation\"] = dataset[\"test\"]\n",
+        "del(dataset[\"test\"])\n",
+        "\n",
+        "classes = dataset[\"train\"].features[\"label\"].names\n",
+        "dataset = dataset.map(\n",
+        "    lambda x: {\"text_label\": [classes[label] for label in x[\"label\"]]},\n",
+        "    batched=True,\n",
+        "    num_proc=1,\n",
+        "    \n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "6b7ea44c",
+      "metadata": {
+        "id": "6b7ea44c",
+        "outputId": "a27a9252-eb13-48ca-cd9e-4953c8bcb75d",
+        "colab": {
+          "referenced_widgets": [
+            "b718fba0f1514025a0ca22e7f780a2fc",
+            "0271e1cc4e2d43c69d4959e46eddec9a",
+            "601fb3752e134641b28da908d4e7b65a",
+            "e002207d6982491cbef196f25fc891f8",
+            "a4857f97132a41acbe4535b03cd8d94a",
+            "d5b57d3c74d14e5d80d1ef634c103a40",
+            "3786a8205e004fe9a5e069acae422410",
+            "05cbd69de3664b82b95e06f72f12a55e",
+            "c149c1c53e9d44008a86944ef8c261c5",
+            "1d11cb45c5cb472aa86722e4dbb8c085",
+            "34142a8e97594931b316970911679e55",
+            "f48454eadbfb4953b719bdf44555c90e",
+            "3c5affff513341b29e6a2c1c90bfe334",
+            "0bbeca449a814d95bec438a9141b2b6b",
+            "a10078c15aae4ec6a849f1b58c6b1cc2",
+            "06e8fd84d6224e5096088d66aad71961",
+            "456429d05857411f8b78f69e8860bf58",
+            "f00b73eb32374c33882c1bfc49822e44",
+            "bc24304c057d4b5898e832818de55caa",
+            "40b951e3a06348a39c7c778aa12f8385",
+            "223848818aff4af1ab5d5e14271408e3",
+            "4b4b31109a9746e88ffa9b47bab00e53"
+          ],
+          "base_uri": "https://localhost:8080/",
+          "height": 81
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Running tokenizer on dataset:   0%|          | 0/3 [00:00<?, ?ba/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "b718fba0f1514025a0ca22e7f780a2fc"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Running tokenizer on dataset:   0%|          | 0/1 [00:00<?, ?ba/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "f48454eadbfb4953b719bdf44555c90e"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# data preprocessing\n",
+        "text_column = \"sentence\"\n",
+        "label_column = \"text_label\"\n",
+        "max_length=128\n",
+        "def preprocess_function(examples):\n",
+        "    inputs = examples[text_column]\n",
+        "    targets = examples[label_column]\n",
+        "    model_inputs = tokenizer(inputs, max_length=max_length, padding=\"max_length\", truncation=True, return_tensors=\"pt\")\n",
+        "    labels = tokenizer(targets, max_length=3, padding=\"max_length\", truncation=True, return_tensors=\"pt\")\n",
+        "    labels = labels[\"input_ids\"]\n",
+        "    labels[labels==tokenizer.pad_token_id] = -100\n",
+        "    model_inputs[\"labels\"] = labels\n",
+        "    return model_inputs\n",
+        "\n",
+        "processed_datasets = dataset.map(\n",
+        "            preprocess_function,\n",
+        "            batched=True,\n",
+        "            num_proc=1,\n",
+        "            remove_columns=dataset[\"train\"].column_names,\n",
+        "            load_from_cache_file=False,\n",
+        "            desc=\"Running tokenizer on dataset\",\n",
+        "        )\n",
+        "\n",
+        "train_dataset = processed_datasets[\"train\"]\n",
+        "eval_dataset = processed_datasets[\"validation\"]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Train our model! \n",
+        "\n",
+        "Let's now train our model, run the cells below.\n",
+        "Note that for T5 since some layers are kept in `float32` for stability purposes there is no need to call autocast on the trainer."
+      ],
+      "metadata": {
+        "id": "bcNTdVypGEPb"
+      },
+      "id": "bcNTdVypGEPb"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "69c756ac",
+      "metadata": {
+        "id": "69c756ac",
+        "outputId": "f0d605b1-3b5d-4e22-e108-819edc7b0d52",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "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"
+          ]
+        }
+      ],
+      "source": [
+        "from transformers import TrainingArguments, Trainer\n",
+        "\n",
+        "training_args = TrainingArguments(\n",
+        "    \"temp\",\n",
+        "    evaluation_strategy = \"epoch\",\n",
+        "    learning_rate=1e-3,\n",
+        "    gradient_accumulation_steps=1, \n",
+        "    auto_find_batch_size=True,\n",
+        "    num_train_epochs=1,\n",
+        "    save_steps=100,\n",
+        "    save_total_limit=8,\n",
+        ")\n",
+        "trainer = Trainer(\n",
+        "    model=model,\n",
+        "    args=training_args,\n",
+        "    train_dataset=train_dataset,\n",
+        "    eval_dataset=eval_dataset,\n",
+        ")\n",
+        "model.config.use_cache = False  # silence the warnings. Please re-enable for inference!"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "ab52b651",
+      "metadata": {
+        "id": "ab52b651",
+        "outputId": "2da171de-e59a-4945-93cb-5704681e84c1",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 659
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.8/dist-packages/transformers/optimization.py:346: 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 = 2037\n",
+            "  Num Epochs = 1\n",
+            "  Instantaneous batch size per device = 8\n",
+            "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
+            "  Gradient Accumulation steps = 1\n",
+            "  Total optimization steps = 255\n",
+            "  Number of trainable parameters = 4718592\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"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "    <div>\n",
+              "      \n",
+              "      <progress value='255' max='255' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+              "      [255/255 06:13, Epoch 1/1]\n",
+              "    </div>\n",
+              "    <table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              " <tr style=\"text-align: left;\">\n",
+              "      <th>Epoch</th>\n",
+              "      <th>Training Loss</th>\n",
+              "      <th>Validation Loss</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <td>1</td>\n",
+              "      <td>No log</td>\n",
+              "      <td>0.017228</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table><p>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Saving model checkpoint to temp/checkpoint-100\n",
+            "Trainer.model is not a `PreTrainedModel`, only saving its state dict.\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",
+            "Saving model checkpoint to temp/checkpoint-200\n",
+            "Trainer.model is not a `PreTrainedModel`, only saving its state dict.\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",
+            "***** Running Evaluation *****\n",
+            "  Num examples = 227\n",
+            "  Batch size = 8\n",
+            "\n",
+            "\n",
+            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
+            "\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "TrainOutput(global_step=255, training_loss=0.2569344015682445, metrics={'train_runtime': 377.3565, 'train_samples_per_second': 5.398, 'train_steps_per_second': 0.676, 'total_flos': 1181084919791616.0, 'train_loss': 0.2569344015682445, 'epoch': 1.0})"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ],
+      "source": [
+        "trainer.train()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Evaluate our model"
+      ],
+      "metadata": {
+        "id": "r98VtofiGXtO"
+      },
+      "id": "r98VtofiGXtO"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "id": "c95d6173",
+      "metadata": {
+        "id": "c95d6173",
+        "outputId": "ed03a1dc-597a-4053-99d6-eca2cc6da253",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Generate config GenerationConfig {\n",
+            "  \"_from_model_config\": true,\n",
+            "  \"decoder_start_token_id\": 0,\n",
+            "  \"eos_token_id\": 1,\n",
+            "  \"pad_token_id\": 0,\n",
+            "  \"transformers_version\": \"4.27.0.dev0\",\n",
+            "  \"use_cache\": false\n",
+            "}\n",
+            "\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",
+            "/usr/local/lib/python3.8/dist-packages/transformers/generation/utils.py:1374: 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": [
+            "input sentence:  In January-September 2009 , the Group 's net interest income increased to EUR 112.4 mn from EUR 74.3 mn in January-September 2008 .\n",
+            " output prediction:  ['positive']\n"
+          ]
+        }
+      ],
+      "source": [
+        "model.eval()\n",
+        "input_text = \"In January-September 2009 , the Group 's net interest income increased to EUR 112.4 mn from EUR 74.3 mn in January-September 2008 .\"\n",
+        "inputs = tokenizer(input_text, return_tensors=\"pt\")\n",
+        "\n",
+        "outputs = model.generate(input_ids=inputs[\"input_ids\"], max_new_tokens=10)\n",
+        "\n",
+        "print(\"input sentence: \", input_text)\n",
+        "print(\" output prediction: \", tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Share your adapters on 🤗 Hub"
+      ],
+      "metadata": {
+        "id": "9QqBlwzoGZ3f"
+      },
+      "id": "9QqBlwzoGZ3f"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Once you have trained your adapter, you can easily share it on the Hub using the method `push_to_hub` . Note that only the adapter weights and config will be pushed"
+      ],
+      "metadata": {
+        "id": "NT-C8SjcKqUx"
+      },
+      "id": "NT-C8SjcKqUx"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "id": "bcbfa1f9",
+      "metadata": {
+        "id": "bcbfa1f9",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 359,
+          "referenced_widgets": [
+            "5bb29f3102954b06bec825f6b3a7aaa7",
+            "90516032070a40979181d1d27db10c4f",
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+            "4dbe49547fe94010ad5a30818cfc35bc",
+            "99091ca45c1b4809ba0a1b01af85f528",
+            "06ec124c3dac4fe6b152fb812d20d86d",
+            "7561c47a97444666816422a0418e1675",
+            "45ab5d7049e34dfd8a067643ae887a31",
+            "434e308cac5847f0bee431c7dbb4c04a",
+            "dd993a4a7cdf40448098544c95468a10"
+          ]
+        },
+        "outputId": "91ef770e-9fc4-4eb2-b02b-24e635101f97"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Token is valid.\n",
+            "Your token has been saved in your configured git credential helpers (store).\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",
+      "source": [
+        "model.push_to_hub(\"ybelkada/flan-t5-large-financial-phrasebank-lora\", use_auth_token=True)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 152,
+          "referenced_widgets": [
+            "00f7d043cb184d69b828c204dac2c0ab",
+            "1e409cd3d3a04b558d989d63f0b3b5f7",
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+            "07bf5d621cf944258aaf13954669df56",
+            "93a0896ca66b4111bc4cabe6e1278440",
+            "cfc78731f7d543ce8529cc254d92ddf5",
+            "eacf8e9ed6e847faae2b8ecab283ddc4",
+            "bef1971d92e6479696e3f9a27a757b8a",
+            "821f2f296acb40ae9bb40fc3faf4103d",
+            "11efd993475a4f2aabe7df605bab04dd",
+            "406e4d8561f64d2a94d93a606d02d7d3"
+          ]
+        },
+        "id": "rFKJ4vHNGkJw",
+        "outputId": "07425379-64ad-47e8-ba8f-8d9dc26252b6"
+      },
+      "id": "rFKJ4vHNGkJw",
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Uploading the following files to ybelkada/flan-t5-large-lora: adapter_model.bin,adapter_config.json\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Upload 1 LFS files:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "00f7d043cb184d69b828c204dac2c0ab"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "adapter_model.bin:   0%|          | 0.00/19.0M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "050de732f51f4af8bb41ab3cad0090a4"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "CommitInfo(commit_url='https://huggingface.co/ybelkada/flan-t5-large-lora/commit/585e4e8cac66188692d506ac107e2d2884c0a4a0', commit_message='Upload model', commit_description='', oid='585e4e8cac66188692d506ac107e2d2884c0a4a0', pr_url=None, pr_revision=None, pr_num=None)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 13
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Load your adapter from the Hub"
+      ],
+      "metadata": {
+        "id": "xHuDmbCYJ89f"
+      },
+      "id": "xHuDmbCYJ89f"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "You can load the model together with the adapter with few lines of code! Check the snippet below"
+      ],
+      "metadata": {
+        "id": "ANFo6DdfKlU3"
+      },
+      "id": "ANFo6DdfKlU3"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import torch\n",
+        "from peft import PeftModel, PeftConfig\n",
+        "from transformers import AutoModelForSeq2SeqLM, AutoTokenizer\n",
+        "\n",
+        "peft_model_id = \"ybelkada/flan-t5-large-financial-phrasebank-lora\"\n",
+        "config = PeftConfig.from_pretrained(peft_model_id)\n",
+        "\n",
+        "model = AutoModelForSeq2SeqLM.from_pretrained(\n",
+        "    config.base_model_name_or_path, \n",
+        "    torch_dtype='auto', \n",
+        "    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)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "j097aaPWJ-9u",
+        "outputId": "a1479feb-86ad-4589-802b-91d498c0abbb"
+      },
+      "id": "j097aaPWJ-9u",
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--google--flan-t5-large/snapshots/1ba3cccf9952e874322ea1e39d0ccf42fc7842b4/config.json\n",
+            "Model config T5Config {\n",
+            "  \"_name_or_path\": \"google/flan-t5-large\",\n",
+            "  \"architectures\": [\n",
+            "    \"T5ForConditionalGeneration\"\n",
+            "  ],\n",
+            "  \"d_ff\": 2816,\n",
+            "  \"d_kv\": 64,\n",
+            "  \"d_model\": 1024,\n",
+            "  \"decoder_start_token_id\": 0,\n",
+            "  \"dense_act_fn\": \"gelu_new\",\n",
+            "  \"dropout_rate\": 0.1,\n",
+            "  \"eos_token_id\": 1,\n",
+            "  \"feed_forward_proj\": \"gated-gelu\",\n",
+            "  \"initializer_factor\": 1.0,\n",
+            "  \"is_encoder_decoder\": true,\n",
+            "  \"is_gated_act\": true,\n",
+            "  \"layer_norm_epsilon\": 1e-06,\n",
+            "  \"model_type\": \"t5\",\n",
+            "  \"n_positions\": 512,\n",
+            "  \"num_decoder_layers\": 24,\n",
+            "  \"num_heads\": 16,\n",
+            "  \"num_layers\": 24,\n",
+            "  \"output_past\": true,\n",
+            "  \"pad_token_id\": 0,\n",
+            "  \"relative_attention_max_distance\": 128,\n",
+            "  \"relative_attention_num_buckets\": 32,\n",
+            "  \"tie_word_embeddings\": false,\n",
+            "  \"transformers_version\": \"4.27.0.dev0\",\n",
+            "  \"use_cache\": true,\n",
+            "  \"vocab_size\": 32128\n",
+            "}\n",
+            "\n",
+            "loading weights file pytorch_model.bin from cache at /root/.cache/huggingface/hub/models--google--flan-t5-large/snapshots/1ba3cccf9952e874322ea1e39d0ccf42fc7842b4/pytorch_model.bin\n",
+            "Generate config GenerationConfig {\n",
+            "  \"_from_model_config\": true,\n",
+            "  \"decoder_start_token_id\": 0,\n",
+            "  \"eos_token_id\": 1,\n",
+            "  \"pad_token_id\": 0,\n",
+            "  \"transformers_version\": \"4.27.0.dev0\"\n",
+            "}\n",
+            "\n",
+            "All model checkpoint weights were used when initializing T5ForConditionalGeneration.\n",
+            "\n",
+            "All the weights of T5ForConditionalGeneration were initialized from the model checkpoint at google/flan-t5-large.\n",
+            "If your task is similar to the task the model of the checkpoint was trained on, you can already use T5ForConditionalGeneration for predictions without further training.\n",
+            "loading configuration file generation_config.json from cache at /root/.cache/huggingface/hub/models--google--flan-t5-large/snapshots/1ba3cccf9952e874322ea1e39d0ccf42fc7842b4/generation_config.json\n",
+            "Generate config GenerationConfig {\n",
+            "  \"_from_model_config\": true,\n",
+            "  \"decoder_start_token_id\": 0,\n",
+            "  \"eos_token_id\": 1,\n",
+            "  \"pad_token_id\": 0,\n",
+            "  \"transformers_version\": \"4.27.0.dev0\"\n",
+            "}\n",
+            "\n",
+            "loading file spiece.model from cache at /root/.cache/huggingface/hub/models--google--flan-t5-large/snapshots/1ba3cccf9952e874322ea1e39d0ccf42fc7842b4/spiece.model\n",
+            "loading file tokenizer.json from cache at /root/.cache/huggingface/hub/models--google--flan-t5-large/snapshots/1ba3cccf9952e874322ea1e39d0ccf42fc7842b4/tokenizer.json\n",
+            "loading file added_tokens.json from cache at None\n",
+            "loading file special_tokens_map.json from cache at /root/.cache/huggingface/hub/models--google--flan-t5-large/snapshots/1ba3cccf9952e874322ea1e39d0ccf42fc7842b4/special_tokens_map.json\n",
+            "loading file tokenizer_config.json from cache at /root/.cache/huggingface/hub/models--google--flan-t5-large/snapshots/1ba3cccf9952e874322ea1e39d0ccf42fc7842b4/tokenizer_config.json\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.eval()\n",
+        "input_text = \"In January-September 2009 , the Group 's net interest income increased to EUR 112.4 mn from EUR 74.3 mn in January-September 2008 .\"\n",
+        "inputs = tokenizer(input_text, return_tensors=\"pt\")\n",
+        "\n",
+        "outputs = model.generate(input_ids=inputs[\"input_ids\"], max_new_tokens=10)\n",
+        "\n",
+        "print(\"input sentence: \", input_text)\n",
+        "print(\" output prediction: \", tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "jmjwWYt0KI_I",
+        "outputId": "466e79b6-c5a8-4a03-9a17-eeb2bc27e9ac"
+      },
+      "id": "jmjwWYt0KI_I",
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Generate config GenerationConfig {\n",
+            "  \"_from_model_config\": true,\n",
+            "  \"decoder_start_token_id\": 0,\n",
+            "  \"eos_token_id\": 1,\n",
+            "  \"pad_token_id\": 0,\n",
+            "  \"transformers_version\": \"4.27.0.dev0\"\n",
+            "}\n",
+            "\n",
+            "/usr/local/lib/python3.8/dist-packages/transformers/generation/utils.py:1374: 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": [
+            "input sentence:  In January-September 2009 , the Group 's net interest income increased to EUR 112.4 mn from EUR 74.3 mn in January-September 2008 .\n",
+            " output prediction:  ['positive']\n"
+          ]
+        }
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "machine_shape": "hm",
+      "provenance": []
+    },
+    "gpuClass": "standard",
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
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\ No newline at end of file