diff --git a/examples/int8_training/PEFT_FlanT5_large_int8.ipynb b/examples/int8_training/PEFT_FlanT5_large_int8.ipynb
index 2856463..a12209d 100644
--- a/examples/int8_training/PEFT_FlanT5_large_int8.ipynb
+++ b/examples/int8_training/PEFT_FlanT5_large_int8.ipynb
@@ -2,40 +2,40 @@
   "cells": [
     {
       "cell_type": "markdown",
-      "source": [
-        "# Fine-tune FLAN-T5 using `bitsandbytes`, `peft` & `transformers` 🤗 "
-      ],
+      "id": "lw1cWgq-DI5k",
       "metadata": {
         "id": "lw1cWgq-DI5k"
       },
-      "id": "lw1cWgq-DI5k"
+      "source": [
+        "# Fine-tune FLAN-T5 using `bitsandbytes`, `peft` & `transformers` 🤗 "
+      ]
     },
     {
       "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!"
-      ],
+      "id": "kBFPA3-aDT7H",
       "metadata": {
         "id": "kBFPA3-aDT7H"
       },
-      "id": "kBFPA3-aDT7H"
+      "source": [
+        "In this notebook we will see how to properly use `peft` , `transformers` & `bitsandbytes` to fine-tune `flan-t5-large` in a google colab!\n",
+        "\n",
+        "We will finetune the model on [`financial_phrasebank`](https://huggingface.co/datasets/financial_phrasebank) dataset, that consists of pairs of text-labels to classify financial-related sentences, if they are either `positive`, `neutral` or `negative`."
+      ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "## Install requirements"
-      ],
+      "id": "ShAuuHCDDkvk",
       "metadata": {
         "id": "ShAuuHCDDkvk"
       },
-      "id": "ShAuuHCDDkvk"
+      "source": [
+        "## Install requirements"
+      ]
     },
     {
       "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"
-      ],
+      "execution_count": null,
+      "id": "DRQ4ZrJTDkSy",
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -43,12 +43,10 @@
         "id": "DRQ4ZrJTDkSy",
         "outputId": "31b108ee-a34c-4a1f-a970-6fa1809b64c5"
       },
-      "id": "DRQ4ZrJTDkSy",
-      "execution_count": 1,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "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",
@@ -68,24 +66,27 @@
             "  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/younesbelkada/peft.git@int8-wrapper"
       ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "## Import model and tokenizer"
-      ],
+      "id": "QBdCIrizDxFw",
       "metadata": {
         "id": "QBdCIrizDxFw"
       },
-      "id": "QBdCIrizDxFw"
+      "source": [
+        "## Import model and tokenizer"
+      ]
     },
     {
       "cell_type": "code",
-      "execution_count": 2,
+      "execution_count": null,
       "id": "dd3c5acc",
       "metadata": {
-        "id": "dd3c5acc",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 350,
@@ -169,47 +170,48 @@
             "608e9f7a14054573b9bd07f0f74b6345"
           ]
         },
+        "id": "dd3c5acc",
         "outputId": "fab398b4-dace-4f44-89e5-687ec74b18eb"
       },
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "59782bac41834abbbd6cbd6614c2e57b",
+              "version_major": 2,
+              "version_minor": 0
+            },
             "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": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "stream",
           "name": "stderr",
+          "output_type": "stream",
           "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": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "dbdb787728184aa1a6906f96c5e6f929",
+              "version_major": 2,
+              "version_minor": 0
+            },
             "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": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "\n",
             "===================================BUG REPORT===================================\n",
@@ -218,74 +220,74 @@
           ]
         },
         {
-          "output_type": "display_data",
           "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "aa2c51ad05c14a02a13e5c047779fc05",
+              "version_major": 2,
+              "version_minor": 0
+            },
             "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": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "1b839f5b472e44148c9e02a12550fade",
+              "version_major": 2,
+              "version_minor": 0
+            },
             "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": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "65aacfc888014bdea55253428b2569c0",
+              "version_major": 2,
+              "version_minor": 0
+            },
             "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": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "d7d9e2e2090d4226ad89e5ba9cec33df",
+              "version_major": 2,
+              "version_minor": 0
+            },
             "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": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "0363c4d4dc1449148b09061763ea119a",
+              "version_major": 2,
+              "version_minor": 0
+            },
             "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": {}
+          "metadata": {},
+          "output_type": "display_data"
         }
       ],
       "source": [
@@ -310,27 +312,31 @@
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "## Prepare model for training"
-      ],
+      "id": "VwcHieQzD_dl",
       "metadata": {
         "id": "VwcHieQzD_dl"
       },
-      "id": "VwcHieQzD_dl"
+      "source": [
+        "## Prepare model for training"
+      ]
     },
     {
       "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`"
-      ],
+      "id": "4o3ePxrjEDzv",
       "metadata": {
         "id": "4o3ePxrjEDzv"
       },
-      "id": "4o3ePxrjEDzv"
+      "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` that will: \n",
+        "- Cast the layer norm in `float32` for stability purposes\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\n",
+        "- Cast the output logits in `float32` for smoother sampling during the sampling procedure"
+      ]
     },
     {
       "cell_type": "code",
-      "execution_count": 3,
+      "execution_count": null,
       "id": "1629ebcb",
       "metadata": {
         "id": "1629ebcb"
@@ -344,31 +350,31 @@
     },
     {
       "cell_type": "markdown",
+      "id": "iCpAgawAEieu",
+      "metadata": {
+        "id": "iCpAgawAEieu"
+      },
       "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,
+      "execution_count": null,
       "id": "17566ae3",
       "metadata": {
-        "id": "17566ae3",
-        "outputId": "66cbd0f3-815d-4d68-c0a3-6b2e5f46b021",
         "colab": {
           "base_uri": "https://localhost:8080/"
-        }
+        },
+        "id": "17566ae3",
+        "outputId": "66cbd0f3-815d-4d68-c0a3-6b2e5f46b021"
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "trainable params: 4718592 || all params: 787868672 || trainable%: 0.5989059049678777\n"
           ]
@@ -408,24 +414,34 @@
     },
     {
       "cell_type": "markdown",
+      "id": "mGkwIgNXyS7U",
+      "metadata": {
+        "id": "mGkwIgNXyS7U"
+      },
       "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."
-      ],
+        "As you can see, here we are only training 0.6% of the parameters of the model! This is a huge memory gain that will enable us to fine-tune the model without any memory issue."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "HsG0x6Z7FwjZ",
       "metadata": {
         "id": "HsG0x6Z7FwjZ"
       },
-      "id": "HsG0x6Z7FwjZ"
+      "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. We will load the split `sentences_allagree`, which corresponds according to the model card to the split where there is a 100% annotator agreement."
+      ]
     },
     {
       "cell_type": "code",
-      "execution_count": 5,
+      "execution_count": null,
       "id": "242cdfae",
       "metadata": {
-        "id": "242cdfae",
-        "outputId": "3122a25f-4299-4c3e-cd93-3a507f9f0d91",
         "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297,
           "referenced_widgets": [
             "ce0213e9d6aa45c5a9ac9954fbe15f62",
             "7cfda0921e5a4f378e90e057447f3b3d",
@@ -515,137 +531,137 @@
             "2ec84788d5dd4a04a24ccf66dad457ad",
             "6f30253108fb4dce9c3de029457ef6f1",
             "1fa2a7e3ff3c4c99ab95e96a28624846"
-          ],
-          "base_uri": "https://localhost:8080/",
-          "height": 297
-        }
+          ]
+        },
+        "id": "242cdfae",
+        "outputId": "3122a25f-4299-4c3e-cd93-3a507f9f0d91"
       },
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "ce0213e9d6aa45c5a9ac9954fbe15f62",
+              "version_major": 2,
+              "version_minor": 0
+            },
             "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": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "f2dc5e8a31c348358aca916274899e8b",
+              "version_major": 2,
+              "version_minor": 0
+            },
             "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": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "94437f56e5a44fa3bb08c9d798b2eaeb",
+              "version_major": 2,
+              "version_minor": 0
+            },
             "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": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "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": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "cce112d791dd4b748908756e785ab555",
+              "version_major": 2,
+              "version_minor": 0
+            },
             "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": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "41d8dbe04d0c4ec4a9d19662f9e11920",
+              "version_major": 2,
+              "version_minor": 0
+            },
             "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": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "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": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "26e1bfb23d3a4797a8dbb9c4ed2aeb22",
+              "version_major": 2,
+              "version_minor": 0
+            },
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-              "version_major": 2,
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-              "model_id": "26e1bfb23d3a4797a8dbb9c4ed2aeb22"
-            }
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "283bdd8a4daf4f5ca1ec6e9ed19c46ec",
+              "version_major": 2,
+              "version_minor": 0
+            },
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-              "model_id": "283bdd8a4daf4f5ca1ec6e9ed19c46ec"
-            }
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
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+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "8d35a041dbfb4747aea427e76890551a",
+              "version_major": 2,
+              "version_minor": 0
+            },
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               "  0%|          | 0/1 [00:00<?, ?ba/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "8d35a041dbfb4747aea427e76890551a"
-            }
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         }
       ],
       "source": [
@@ -664,14 +680,24 @@
         ")"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "id": "qzwyi-Z9yzRF",
+      "metadata": {
+        "id": "qzwyi-Z9yzRF"
+      },
+      "source": [
+        "Let's also apply some pre-processing of the input data, the labels needs to be pre-processed, the tokens corresponding to `pad_token_id` needs to be set to `-100` so that the `CrossEntropy` loss associated with the model will correctly ignore these tokens."
+      ]
+    },
     {
       "cell_type": "code",
-      "execution_count": 6,
+      "execution_count": null,
       "id": "6b7ea44c",
       "metadata": {
-        "id": "6b7ea44c",
-        "outputId": "a27a9252-eb13-48ca-cd9e-4953c8bcb75d",
         "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 81,
           "referenced_widgets": [
             "b718fba0f1514025a0ca22e7f780a2fc",
             "0271e1cc4e2d43c69d4959e46eddec9a",
@@ -695,39 +721,39 @@
             "40b951e3a06348a39c7c778aa12f8385",
             "223848818aff4af1ab5d5e14271408e3",
             "4b4b31109a9746e88ffa9b47bab00e53"
-          ],
-          "base_uri": "https://localhost:8080/",
-          "height": 81
-        }
+          ]
+        },
+        "id": "6b7ea44c",
+        "outputId": "a27a9252-eb13-48ca-cd9e-4953c8bcb75d"
       },
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "b718fba0f1514025a0ca22e7f780a2fc",
+              "version_major": 2,
+              "version_minor": 0
+            },
             "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": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "f48454eadbfb4953b719bdf44555c90e",
+              "version_major": 2,
+              "version_minor": 0
+            },
             "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": {}
+          "metadata": {},
+          "output_type": "display_data"
         }
       ],
       "source": [
@@ -760,32 +786,32 @@
     },
     {
       "cell_type": "markdown",
+      "id": "bcNTdVypGEPb",
+      "metadata": {
+        "id": "bcNTdVypGEPb"
+      },
       "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,
+      "execution_count": null,
       "id": "69c756ac",
       "metadata": {
-        "id": "69c756ac",
-        "outputId": "f0d605b1-3b5d-4e22-e108-819edc7b0d52",
         "colab": {
           "base_uri": "https://localhost:8080/"
-        }
+        },
+        "id": "69c756ac",
+        "outputId": "f0d605b1-3b5d-4e22-e108-819edc7b0d52"
       },
       "outputs": [
         {
-          "output_type": "stream",
           "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"
           ]
@@ -815,20 +841,20 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 8,
+      "execution_count": null,
       "id": "ab52b651",
       "metadata": {
-        "id": "ab52b651",
-        "outputId": "2da171de-e59a-4945-93cb-5704681e84c1",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 659
-        }
+        },
+        "id": "ab52b651",
+        "outputId": "2da171de-e59a-4945-93cb-5704681e84c1"
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stderr",
+          "output_type": "stream",
           "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",
@@ -845,11 +871,7 @@
           ]
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ],
             "text/html": [
               "\n",
               "    <div>\n",
@@ -873,13 +895,17 @@
               "    </tr>\n",
               "  </tbody>\n",
               "</table><p>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
             ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "stream",
           "name": "stderr",
+          "output_type": "stream",
           "text": [
             "Saving model checkpoint to temp/checkpoint-100\n",
             "Trainer.model is not a `PreTrainedModel`, only saving its state dict.\n",
@@ -900,14 +926,14 @@
           ]
         },
         {
-          "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})"
             ]
           },
+          "execution_count": 8,
           "metadata": {},
-          "execution_count": 8
+          "output_type": "execute_result"
         }
       ],
       "source": [
@@ -916,29 +942,39 @@
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "## Evaluate our model"
-      ],
+      "id": "r98VtofiGXtO",
       "metadata": {
         "id": "r98VtofiGXtO"
       },
-      "id": "r98VtofiGXtO"
+      "source": [
+        "## Qualitatively test our model"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "NIm7z3UNzGPP",
+      "metadata": {
+        "id": "NIm7z3UNzGPP"
+      },
+      "source": [
+        "Let's have a quick qualitative evaluation of the model, by taking a sample from the dataset that corresponds to a positive label. Run your generation similarly as you were running your model from `transformers`:"
+      ]
     },
     {
       "cell_type": "code",
-      "execution_count": 11,
+      "execution_count": null,
       "id": "c95d6173",
       "metadata": {
-        "id": "c95d6173",
-        "outputId": "ed03a1dc-597a-4053-99d6-eca2cc6da253",
         "colab": {
           "base_uri": "https://localhost:8080/"
-        }
+        },
+        "id": "c95d6173",
+        "outputId": "ed03a1dc-597a-4053-99d6-eca2cc6da253"
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stderr",
+          "output_type": "stream",
           "text": [
             "Generate config GenerationConfig {\n",
             "  \"_from_model_config\": true,\n",
@@ -956,8 +992,8 @@
           ]
         },
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "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"
@@ -977,30 +1013,29 @@
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "## Share your adapters on 🤗 Hub"
-      ],
+      "id": "9QqBlwzoGZ3f",
       "metadata": {
         "id": "9QqBlwzoGZ3f"
       },
-      "id": "9QqBlwzoGZ3f"
+      "source": [
+        "## Share your adapters on 🤗 Hub"
+      ]
     },
     {
       "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"
-      ],
+      "id": "NT-C8SjcKqUx",
       "metadata": {
         "id": "NT-C8SjcKqUx"
       },
-      "id": "NT-C8SjcKqUx"
+      "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"
+      ]
     },
     {
       "cell_type": "code",
-      "execution_count": 12,
+      "execution_count": null,
       "id": "bcbfa1f9",
       "metadata": {
-        "id": "bcbfa1f9",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 359,
@@ -1024,12 +1059,13 @@
             "dd993a4a7cdf40448098544c95468a10"
           ]
         },
+        "id": "bcbfa1f9",
         "outputId": "91ef770e-9fc4-4eb2-b02b-24e635101f97"
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "Token is valid.\n",
             "Your token has been saved in your configured git credential helpers (store).\n",
@@ -1046,9 +1082,8 @@
     },
     {
       "cell_type": "code",
-      "source": [
-        "model.push_to_hub(\"ybelkada/flan-t5-large-financial-phrasebank-lora\", use_auth_token=True)"
-      ],
+      "execution_count": null,
+      "id": "rFKJ4vHNGkJw",
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -1081,78 +1116,85 @@
         "id": "rFKJ4vHNGkJw",
         "outputId": "07425379-64ad-47e8-ba8f-8d9dc26252b6"
       },
-      "id": "rFKJ4vHNGkJw",
-      "execution_count": 13,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stderr",
+          "output_type": "stream",
           "text": [
             "Uploading the following files to ybelkada/flan-t5-large-lora: adapter_model.bin,adapter_config.json\n"
           ]
         },
         {
-          "output_type": "display_data",
           "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "00f7d043cb184d69b828c204dac2c0ab",
+              "version_major": 2,
+              "version_minor": 0
+            },
             "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": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "050de732f51f4af8bb41ab3cad0090a4",
+              "version_major": 2,
+              "version_minor": 0
+            },
             "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": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "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)"
             ]
           },
+          "execution_count": 13,
           "metadata": {},
-          "execution_count": 13
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "model.push_to_hub(\"ybelkada/flan-t5-large-financial-phrasebank-lora\", use_auth_token=True)"
       ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "## Load your adapter from the Hub"
-      ],
+      "id": "xHuDmbCYJ89f",
       "metadata": {
         "id": "xHuDmbCYJ89f"
       },
-      "id": "xHuDmbCYJ89f"
+      "source": [
+        "## Load your adapter from the Hub"
+      ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "You can load the model together with the adapter with few lines of code! Check the snippet below"
-      ],
+      "id": "ANFo6DdfKlU3",
       "metadata": {
         "id": "ANFo6DdfKlU3"
       },
-      "id": "ANFo6DdfKlU3"
+      "source": [
+        "You can load the model together with the adapter with few lines of code! Check the snippet below to load the adapter from the Hub and run the example evaluation!"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "id": "j097aaPWJ-9u",
+      "metadata": {
+        "id": "j097aaPWJ-9u"
+      },
+      "outputs": [],
       "source": [
         "import torch\n",
         "from peft import PeftModel, PeftConfig\n",
@@ -1170,97 +1212,12 @@
         "\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))"
-      ],
+      "execution_count": null,
+      "id": "jmjwWYt0KI_I",
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -1268,12 +1225,10 @@
         "id": "jmjwWYt0KI_I",
         "outputId": "466e79b6-c5a8-4a03-9a17-eeb2bc27e9ac"
       },
-      "id": "jmjwWYt0KI_I",
-      "execution_count": 18,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stderr",
+          "output_type": "stream",
           "text": [
             "Generate config GenerationConfig {\n",
             "  \"_from_model_config\": true,\n",
@@ -1288,13 +1243,23 @@
           ]
         },
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "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))"
       ]
     }
   ],
@@ -1306,7 +1271,7 @@
     },
     "gpuClass": "standard",
     "kernelspec": {
-      "display_name": "Python 3 (ipykernel)",
+      "display_name": "fix-test",
       "language": "python",
       "name": "python3"
     },
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