diff --git a/notebooks/07_Complex_Adapter_Configuration.ipynb b/notebooks/07_Complex_Adapter_Configuration.ipynb
index 0a7fcc0624..82b994207d 100644
--- a/notebooks/07_Complex_Adapter_Configuration.ipynb
+++ b/notebooks/07_Complex_Adapter_Configuration.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "# 7️⃣ Training Complex Adapter Combinations\n",
         "\n",
-        "In this notebook, we explore how to easily configure complex combinations of different adapter methods with `ConfigUnion`. We show how to re-build the adapter setups used in [He et al., 2022](https://arxiv.org/pdf/2110.04366.pdf) (Mix-and-Match adapter) and [Mao et al., 2022](https://arxiv.org/pdf/2110.07577.pdf) (UniPELT).\n",
+        "In this notebook, we explore how to easily configure complex combinations of different adapter methods with `ConfigUnion`. We show how to re-build the adapter setup used in Mao et al., 2022](https://arxiv.org/pdf/2110.07577.pdf) (UniPELT).\n",
         "For a basic introduction into the training setup with _Adapters_, please first refer to [the introductory training notebook](https://colab.research.google.com/github/Adapter-Hub/adapters/blob/main/notebooks/01_Adapter_Training.ipynb).\n",
         "\n",
         "As training task, we chose abstractive summarization on the **XSum** dataset ([Narayan et al., 2018](https://arxiv.org/pdf/1808.08745.pdf)). As base model, we select **T5**."
@@ -37,8 +37,8 @@
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
             "  Building wheel for rouge_score (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
@@ -57,12 +57,14 @@
       "source": [
         "## Dataset Preprocessing\n",
         "\n",
-        "Before we start to train our adapter, we first prepare the training data. Our training dataset can be loaded via HuggingFace `datasets` using one line of code:"
+        "Before we start to train our adapter, we first prepare the training data. The XSum dataset can be loaded via HuggingFace `datasets`.\n",
+        "\n",
+        "**Note:** To keep training time short in this notebook, we only load a small subset of the full dataset. For good results, make sure to train on the full dataset."
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 2,
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -161,130 +163,7 @@
         "id": "LCLS9RYeK83s",
         "outputId": "f28e27b1-a459-47e1-e1cf-3f2283e893bb"
       },
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Downloading builder script:   0%|          | 0.00/5.76k [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "799ddb94fff34164928166a3b9c1f25f"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Downloading readme:   0%|          | 0.00/6.24k [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "7e2da1d810d740e6a23079a795de3e5e"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Downloading data files:   0%|          | 0/2 [00:00<?, ?it/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "73d1cd4c73ea49039c1d9ce28d211777"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Downloading data:   0%|          | 0.00/255M [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "6fa3dad9dad1494e857cb69a063a9d85"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Downloading data:   0%|          | 0.00/1.00M [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "59dd13c441ce406b96147de93e2e4f85"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Generating train split:   0%|          | 0/204045 [00:00<?, ? examples/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "000408980ba541df83a1bb54a38b791a"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Generating validation split:   0%|          | 0/11332 [00:00<?, ? examples/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "155a28d0e35b41048302227e762f48a0"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Generating test split:   0%|          | 0/11334 [00:00<?, ? examples/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "2326ea1997844aec8f647dd0bd76949a"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "5000"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 2
-        }
-      ],
+      "outputs": [],
       "source": [
         "from datasets import load_dataset\n",
         "\n",
@@ -299,7 +178,7 @@
         "id": "8DnDtZHpK-_t"
       },
       "source": [
-        "Every dataset sample has an input text and a short summary:"
+        "Every dataset sample has an input document and a short summary:"
       ]
     },
     {
@@ -314,7 +193,6 @@
       },
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "{'document': 'The full cost of damage in Newton Stewart, one of the areas worst affected, is still being assessed.\\nRepair work is ongoing in Hawick and many roads in Peeblesshire remain badly affected by standing water.\\nTrains on the west coast mainline face disruption due to damage at the Lamington Viaduct.\\nMany businesses and householders were affected by flooding in Newton Stewart after the River Cree overflowed into the town.\\nFirst Minister Nicola Sturgeon visited the area to inspect the damage.\\nThe waters breached a retaining wall, flooding many commercial properties on Victoria Street - the main shopping thoroughfare.\\nJeanette Tate, who owns the Cinnamon Cafe which was badly affected, said she could not fault the multi-agency response once the flood hit.\\nHowever, she said more preventative work could have been carried out to ensure the retaining wall did not fail.\\n\"It is difficult but I do think there is so much publicity for Dumfries and the Nith - and I totally appreciate that - but it is almost like we\\'re neglected or forgotten,\" she said.\\n\"That may not be true but it is perhaps my perspective over the last few days.\\n\"Why were you not ready to help us a bit more when the warning and the alarm alerts had gone out?\"\\nMeanwhile, a flood alert remains in place across the Borders because of the constant rain.\\nPeebles was badly hit by problems, sparking calls to introduce more defences in the area.\\nScottish Borders Council has put a list on its website of the roads worst affected and drivers have been urged not to ignore closure signs.\\nThe Labour Party\\'s deputy Scottish leader Alex Rowley was in Hawick on Monday to see the situation first hand.\\nHe said it was important to get the flood protection plan right but backed calls to speed up the process.\\n\"I was quite taken aback by the amount of damage that has been done,\" he said.\\n\"Obviously it is heart-breaking for people who have been forced out of their homes and the impact on businesses.\"\\nHe said it was important that \"immediate steps\" were taken to protect the areas most vulnerable and a clear timetable put in place for flood prevention plans.\\nHave you been affected by flooding in Dumfries and Galloway or the Borders? Tell us about your experience of the situation and how it was handled. Email us on selkirk.news@bbc.co.uk or dumfries@bbc.co.uk.',\n",
@@ -322,8 +200,9 @@
               " 'id': '35232142'}"
             ]
           },
+          "execution_count": 3,
           "metadata": {},
-          "execution_count": 3
+          "output_type": "execute_result"
         }
       ],
       "source": [
@@ -332,11 +211,9 @@
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "id": "sYD2HAvSLFwA"
-      },
+      "metadata": {},
       "source": [
-        "Now, we need to encode all dataset samples to valid inputs for our Transformer model. Since we want to train on `t5-small`, we load the corresponding Tokenizer. Using `dataset.map()`, we can pass the full dataset through the tokenizer in batches:"
+        "In this example, we use a `t5-small` model for faster training. Feel free to use a larger checkpoint for better results."
       ]
     },
     {
@@ -350,9 +227,18 @@
         "model_id = \"t5-small\""
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sYD2HAvSLFwA"
+      },
+      "source": [
+        "Now, we need to encode all dataset samples to valid inputs for our Transformer model. We load the tokenizer matching the model we want to train. Using `dataset.map()`, we can pass the full dataset through the tokenizer in batches:"
+      ]
+    },
     {
       "cell_type": "code",
-      "execution_count": 61,
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -385,36 +271,7 @@
         "id": "oDHhicggLHW7",
         "outputId": "255f313c-89af-4f8e-bf4a-b4a19d88f536"
       },
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Map:   0%|          | 0/5000 [00:00<?, ? examples/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "55eaee128e444763af7ea515be45ec0b"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Map:   0%|          | 0/500 [00:00<?, ? examples/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "cb9cedfacba14d5282b1478d55c2075a"
-            }
-          },
-          "metadata": {}
-        }
-      ],
+      "outputs": [],
       "source": [
         "from transformers import AutoTokenizer\n",
         "\n",
@@ -464,7 +321,7 @@
       "source": [
         "## Training\n",
         "\n",
-        "We use a pre-trained T5 model checkpoint from the Hugging Face Hub. We load it with [`AutoAdapterModel`](https://docs.adapterhub.ml/classes/models/auto.html), a class unique to `adapters`. In addition to regular _Transformers_ classes, this class comes with all sorts of adapter-specific functionality, allowing flexible management and configuration of multiple adapters and prediction heads. [Learn more](https://docs.adapterhub.ml/prediction_heads.html#adaptermodel-classes)."
+        "We use a pre-trained T5 model checkpoint from the Hugging Face Hub. We load it with [`AutoAdapterModel`](https://docs.adapterhub.ml/classes/models/auto.html), which comes built-in with all adapter functionality. [Learn more](https://docs.adapterhub.ml/prediction_heads.html#adaptermodel-classes)."
       ]
     },
     {
@@ -480,16 +337,49 @@
         "model = AutoAdapterModel.from_pretrained(model_id)"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "**Now we're ready to build our adapter setup!**\n",
+        "\n",
+        "The UniPELT framework ([Mao et al., 2022](https://arxiv.org/pdf/2110.07577.pdf)) presented one approach of combining multiple types of adapter blocks in a single, combined setup.\n",
+        "Visualized, UniPELT looks roughly like this:\n",
+        "\n",
+        "<div>\n",
+        "<img src=\"https://docs.adapterhub.ml/_images/unipelt.png\" width=\"250\"/>\n",
+        "</div>\n",
+        "\n",
+        "We can see that UniPELT is built from three well-known single adapter methods: 1) LoRA, 2) Prefix Tuning and 3) Sequential Bottleneck.\n",
+        "\n",
+        "_Adapters_ provides an easy way to flexibly build these composed configurations: [`ConfigUnion`](https://docs.adapterhub.ml/classes/adapter_config.html#adapters.ConfigUnion). `ConfigUnion` basically acts as a container holding multiple child adapter configs. [Learn more](https://docs.adapterhub.ml/method_combinations.html).\n",
+        "\n",
+        "With `ConfigUnion`, we can define UniPELT as follows:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from adapters import ConfigUnion, PrefixTuningConfig, SeqBnConfig, LoRAConfig\n",
+        "\n",
+        "config = ConfigUnion(\n",
+        "    LoRAConfig(r=8, use_gating=True),\n",
+        "    PrefixTuningConfig(prefix_length=10, use_gating=True),\n",
+        "    SeqBnConfig(reduction_factor=16, use_gating=True),\n",
+        ")"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
         "id": "QY4Xiy9iLOVO"
       },
       "source": [
-        "**Here comes the important part!**\n",
         "\n",
-        "We add a new adapter to our model by calling `add_adapter()`. We pass a name (`\"xsum\"`) and an adapter configuration. `\"bn_seq\"` denotes a [sequential bottleneck adapter](https://docs.adapterhub.ml/methods.html#bottleneck-adapters) configuration.\n",
-        "_Adapters_ supports a diverse range of different adapter configurations. For example, `config=\"lora\"` can be passed for training a [LoRA](https://docs.adapterhub.ml/methods.html#lora) adapter or `config=\"prefix_tuning\"` for a [prefix tuning](https://docs.adapterhub.ml/methods.html#prefix-tuning). You can find all currently supported configs [here](https://docs.adapterhub.ml/methods.html#prefix-tuning). In this case we use a complex `ConfigUnion` to combine multiple adapter methods to achieve a [unipelt setup](https://docs.adapterhub.ml/method_combinations.html#unipelt)\n",
+        "We now add a new adapter to our model by calling `add_adapter()`. We pass the name (`\"xsum\"`) and the adapter configuration we defined using `ConfigUnion`.\n",
         "\n",
         "Next, we add a seq2seq head. It's convenient to give the prediction head the same name as the adapter. This allows us to activate both together in the next step. The `train_adapter()` method does two things:\n",
         "\n",
@@ -505,19 +395,8 @@
       },
       "outputs": [],
       "source": [
-        "from adapters import ConfigUnion, PrefixTuningConfig, SeqBnConfig, LoRAConfig\n",
-        "\n",
-        "# Add a new unipelt adapter\n",
-        "config = ConfigUnion(\n",
-        "    LoRAConfig(r=8, use_gating=True),\n",
-        "    PrefixTuningConfig(prefix_length=10, use_gating=True),\n",
-        "    SeqBnConfig(reduction_factor=16, use_gating=True),\n",
-        ")\n",
         "model.add_adapter(\"xsum\", config=config)\n",
         "\n",
-        "# Alternatively, e.g.:\n",
-        "# model.add_adapter(\"rotten_tomatoes\", config=\"lora\")\n",
-        "\n",
         "# Add a matching classification head\n",
         "model.add_seq2seq_lm_head(\"xsum\")\n",
         "\n",
@@ -566,6 +445,13 @@
         ")"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Some additional logic for computing metrics:"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": 290,
@@ -578,6 +464,8 @@
         "from evaluate import load\n",
         "import nltk\n",
         "\n",
+        "nltk.download(\"punkt\")\n",
+        "\n",
         "metric = load(\"rouge\")\n",
         "\n",
         "def compute_metrics(eval_pred):\n",
@@ -625,41 +513,6 @@
         ")"
       ]
     },
-    {
-      "cell_type": "code",
-      "source": [
-        "import nltk\n",
-        "nltk.download('punkt')"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "NEHYNBcGke4Q",
-        "outputId": "b09f85f4-c088-4313-b33f-75f61847451f"
-      },
-      "execution_count": 292,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
-            "[nltk_data]   Package punkt is already up-to-date!\n"
-          ]
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "True"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 292
-        }
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -682,11 +535,7 @@
       },
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ],
             "text/html": [
               "\n",
               "    <div>\n",
@@ -770,13 +619,17 @@
               "    </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": [
             "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1273: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n",
             "  warnings.warn(\n",
@@ -787,14 +640,14 @@
           ]
         },
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "TrainOutput(global_step=1878, training_loss=3.978136690279927, metrics={'train_runtime': 985.7264, 'train_samples_per_second': 30.434, 'train_steps_per_second': 1.905, 'total_flos': 5071442595840000.0, 'train_loss': 3.978136690279927, 'epoch': 6.0})"
             ]
           },
+          "execution_count": 293,
           "metadata": {},
-          "execution_count": 293
+          "output_type": "execute_result"
         }
       ],
       "source": [
@@ -823,11 +676,7 @@
       },
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ],
             "text/html": [
               "\n",
               "    <div>\n",
@@ -836,12 +685,15 @@
               "      [32/32 00:36]\n",
               "    </div>\n",
               "    "
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
             ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "{'eval_loss': 3.359184503555298,\n",
@@ -856,8 +708,9 @@
               " 'epoch': 6.0}"
             ]
           },
+          "execution_count": 294,
           "metadata": {},
-          "execution_count": 294
+          "output_type": "execute_result"
         }
       ],
       "source": [
@@ -885,21 +738,21 @@
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stderr",
+          "output_type": "stream",
           "text": [
             "The model 'T5AdapterModel' is not supported for . Supported models are ['BartForConditionalGeneration', 'BigBirdPegasusForConditionalGeneration', 'BlenderbotForConditionalGeneration', 'BlenderbotSmallForConditionalGeneration', 'EncoderDecoderModel', 'FSMTForConditionalGeneration', 'GPTSanJapaneseForConditionalGeneration', 'LEDForConditionalGeneration', 'LongT5ForConditionalGeneration', 'M2M100ForConditionalGeneration', 'MarianMTModel', 'MBartForConditionalGeneration', 'MT5ForConditionalGeneration', 'MvpForConditionalGeneration', 'NllbMoeForConditionalGeneration', 'PegasusForConditionalGeneration', 'PegasusXForConditionalGeneration', 'PLBartForConditionalGeneration', 'ProphetNetForConditionalGeneration', 'SeamlessM4TForTextToText', 'SwitchTransformersForConditionalGeneration', 'T5ForConditionalGeneration', 'UMT5ForConditionalGeneration', 'XLMProphetNetForConditionalGeneration'].\n"
           ]
         },
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "[{'summary_text': 'The Disney Princess has a new film that starred in the film \"The Darkness of'}]"
             ]
           },
+          "execution_count": 295,
           "metadata": {},
-          "execution_count": 295
+          "output_type": "execute_result"
         }
       ],
       "source": [
@@ -965,16 +818,7 @@
         "id": "78jqOTrLR8HN"
       },
       "source": [
-        "This will create a repository _my-awesome-adapter_ under your username, generate a default adapter card as README.md and upload the adapter named `rotten_tomatoes` together with the adapter card to the new repository. Passing `adapterhub_tag` is required to make sure your adapter is features on [adapterhub.ml/explore](https://adapterhub.ml/explore), our Hub page. [Learn more](https://docs.adapterhub.ml/huggingface_hub.html)."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "LLU5O1JER8HN"
-      },
-      "source": [
-        "➡️ Continue with [the next Colab notebook](https://colab.research.google.com/github/Adapter-Hub/adapters/blob/main/notebooks/02_Adapter_Inference.ipynb) to learn how to use adapters from the Hub."
+        "This will create a repository _my-awesome-adapter_ under your username, generate a default adapter card as README.md and upload the adapter named `xsum` together with the adapter card to the new repository. Passing `adapterhub_tag` is required to make sure your adapter is features on [adapterhub.ml/explore](https://adapterhub.ml/explore), our Hub page. [Learn more](https://docs.adapterhub.ml/huggingface_hub.html)."
       ]
     }
   ],
@@ -1001,10 +845,10 @@
     },
     "widgets": {
       "application/vnd.jupyter.widget-state+json": {
-        "799ddb94fff34164928166a3b9c1f25f": {
+        "000408980ba541df83a1bb54a38b791a": {
           "model_module": "@jupyter-widgets/controls",
-          "model_name": "HBoxModel",
           "model_module_version": "1.5.0",
+          "model_name": "HBoxModel",
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diff --git a/notebooks/README.md b/notebooks/README.md
index 9063ded11d..996f20f378 100644
--- a/notebooks/README.md
+++ b/notebooks/README.md
@@ -11,8 +11,6 @@ As adapters is fully compatible with HuggingFace's Transformers, you can also us
 | Notebook        | Description          |   |
 |:----------------|:---------------------|--:|
 | [1: Training an Adapter](https://github.com/Adapter-Hub/adapters/blob/main/notebooks/01_Adapter_Training.ipynb) | How to train a task adapter for a Transformer model | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Adapter-Hub/adapters/blob/main/notebooks/01_Adapter_Training.ipynb) |
-| [1.a: Text Generation](https://github.com/Adapter-Hub/adapters/blob/main/notebooks/Text_Generation_Training.ipynb) | How to train an adapter for language generation. | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Adapter-Hub/adapters/blob/main/notebooks/Text_Generation_Training.ipynb) |
-| [1.b: Training a NER Adapter](https://github.com/Adapter-Hub/adapters/blob/main/notebooks/Adapter_train_NER_with_id2label.ipynb) | How to train an adapter on a named entity recoginition task. | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Adapter-Hub/adapters/blob/main/notebooks/Adapter_train_NER_with_id2label.ipynb) |
 | [2: Using Adapters for Inference](https://github.com/Adapter-Hub/adapters/blob/main/notebooks/02_Adapter_Inference.ipynb) | How to download and use pre-trained adapters from AdapterHub | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Adapter-Hub/adapters/blob/main/notebooks/02_Adapter_Inference.ipynb) |
 
 ## Chapter 2: Modularity & Composition
@@ -22,13 +20,14 @@ As adapters is fully compatible with HuggingFace's Transformers, you can also us
 | [3: Adapter Fusion](https://github.com/Adapter-Hub/adapters/blob/main/notebooks/03_Adapter_Fusion.ipynb) | How to combine multiple pre-trained adapters on a new task using `Fuse` composition. | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Adapter-Hub/adapters/blob/main/notebooks/03_Adapter_Fusion.ipynb) |
 | [4: Cross-lingual Transfer](https://github.com/Adapter-Hub/adapters/blob/main/notebooks/04_Cross_Lingual_Transfer.ipynb) | How to perform zero-shot cross-lingual transfer between tasks using the MAD-X setup (`Stack`). | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Adapter-Hub/adapters/blob/main/notebooks/04_Cross_Lingual_Transfer.ipynb) |
 | [5: Parallel Adapter Inference](https://github.com/Adapter-Hub/adapters/blob/main/notebooks/05_Parallel_Adapter_Inference.ipynb) | Using the `Parallel` composition block for inference. | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Adapter-Hub/adapters/blob/main/notebooks/05_Parallel_Adapter_Inference.ipynb) |
-| [6: Adapter Averaging](https://github.com/Adapter-Hub/adapters/blob/main/notebooks/06_Adapter_Averaging.ipynb) | How to average parameters or outputs of multiple adapters during inference. | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Adapter-Hub/adapters/blob/main/notebooks/06_Adapter_Averaging.ipynb) |
 | [7: Complex Adapter Configuration](https://github.com/Adapter-Hub/adapters/blob/main/notebooks/07_Complex_Adapter_Configuration.ipynb) | How to flexibly combine multiple adapter methods in complex setups using `ConfigUnion`. | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Adapter-Hub/adapters/blob/main/notebooks/07_Complex_Adapter_Configuration.ipynb) |
 
 ## Chapter 3: Additional Notebooks
 
 | Notebook        | Description          |   |
 |:----------------|:---------------------|--:|
+| [Text Generation](https://github.com/Adapter-Hub/adapters/blob/main/notebooks/Text_Generation_Training.ipynb) | How to train an adapter for language generation. | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Adapter-Hub/adapters/blob/main/notebooks/Text_Generation_Training.ipynb) |
+| [Training a NER Adapter](https://github.com/Adapter-Hub/adapters/blob/main/notebooks/Adapter_train_NER_with_id2label.ipynb) | How to train an adapter on a named entity recoginition task. | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Adapter-Hub/adapters/blob/main/notebooks/Adapter_train_NER_with_id2label.ipynb) |
 | [Adapter Drop Training](https://github.com/Adapter-Hub/adapters/blob/main/notebooks/Adapter_Drop_Training.ipynb) | How to train an adapter using AdapterDrop | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Adapter-Hub/adapters/blob/main/notebooks/Adapter_Drop_Training.ipynb) |
 | [Inference example for id2label](https://github.com/Adapter-Hub/adapters/blob/main/notebooks/Adapter_train_NER_with_id2label.ipynb) | How to use the id2label dictionary for inference | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Adapter-Hub/adapters/blob/main/notebooks/Adapter_id2label_inference.ipynb) |
 | [NER on Wikiann](https://github.com/Adapter-Hub/adapters/blob/main/notebooks/08_NER_Wikiann.ipynb) | Evaluating adapters on NER on the wikiann dataset | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Adapter-Hub/adapters/blob/main/notebooks/08_NER_Wikiann.ipynb) |