diff --git a/course/fr/chapter10/section2.ipynb b/course/fr/chapter10/section2.ipynb
new file mode 100644
index 00000000..f7dd5431
--- /dev/null
+++ b/course/fr/chapter10/section2.ipynb
@@ -0,0 +1,65 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kK8kfIfqrgrc"
+      },
+      "source": [
+        "# Configurez votre instance Argilla"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "cGOYFrYyrgrd"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install argilla"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "mWB23rrFrgrf"
+      },
+      "outputs": [],
+      "source": [
+        "import argilla as rg\n",
+        "\n",
+        "HF_TOKEN = \"...\"  # uniquement pour les spaces privés\n",
+        "\n",
+        "client = rg.Argilla(\n",
+        "    api_url=\"...\",\n",
+        "    api_key=\"...\",\n",
+        "    headers={\"Authorization\": f\"Bearer {HF_TOKEN}\"},  # uniquement pour les spaces privés\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "UVHJPCFKrgrg"
+      },
+      "outputs": [],
+      "source": [
+        "client.me"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "Set up your Argilla instance",
+      "provenance": []
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/course/fr/chapter10/section3.ipynb b/course/fr/chapter10/section3.ipynb
new file mode 100644
index 00000000..48a2a5d0
--- /dev/null
+++ b/course/fr/chapter10/section3.ipynb
@@ -0,0 +1,115 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chargez votre jeu de données dans Argilla"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install argilla datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import argilla as rg\n",
+    "\n",
+    "HF_TOKEN = \"...\"  # uniquement pour les spaces privés\n",
+    "\n",
+    "client = rg.Argilla(\n",
+    "    api_url=\"...\",\n",
+    "    api_key=\"...\",\n",
+    "    headers={\"Authorization\": f\"Bearer {HF_TOKEN}\"},  # uniquement pour les spaces privés\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'text': Value(dtype='string', id=None),\n",
+       " 'label': Value(dtype='int64', id=None),\n",
+       " 'label_text': Value(dtype='string', id=None)}"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from datasets import load_dataset\n",
+    "\n",
+    "data = load_dataset(\"SetFit/ag_news\", split=\"train\")\n",
+    "data.features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "settings = rg.Settings(\n",
+    "    fields=[rg.TextField(name=\"text\")],\n",
+    "    questions=[\n",
+    "        rg.LabelQuestion(\n",
+    "            name=\"label\", title=\"Classifier le texte :\", labels=data.unique(\"label_text\")\n",
+    "        ),\n",
+    "        rg.SpanQuestion(\n",
+    "            name=\"entities\",\n",
+    "            title=\"Surligner toutes les entités présentes dans le texte :\",\n",
+    "            labels=[\"PERSON\", \"ORG\", \"LOC\", \"EVENT\"],\n",
+    "            field=\"text\",\n",
+    "        ),\n",
+    "    ],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset = rg.Dataset(name=\"ag_news\", settings=settings)\n",
+    "\n",
+    "dataset.create()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset.records.log(data, mapping={\"label_text\": \"label\"})"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "name": "Load your dataset to Argilla",
+   "provenance": []
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/course/fr/chapter10/section5.ipynb b/course/fr/chapter10/section5.ipynb
new file mode 100644
index 00000000..b2747a79
--- /dev/null
+++ b/course/fr/chapter10/section5.ipynb
@@ -0,0 +1,95 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Utilisez votre jeu de données annoté"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install argilla"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import argilla as rg\n",
+    "\n",
+    "HF_TOKEN = \"...\"  # uniquement pour les spaces privés\n",
+    "\n",
+    "client = rg.Argilla(\n",
+    "    api_url=\"...\",\n",
+    "    api_key=\"...\",\n",
+    "    headers={\"Authorization\": f\"Bearer {HF_TOKEN}\"},  # uniquement pour les spaces privés\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset = client.datasets(name=\"ag_news\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "status_filter = rg.Query(filter=rg.Filter([(\"status\", \"==\", \"completed\")]))\n",
+    "\n",
+    "filtered_records = dataset.records(status_filter)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filtered_records.to_datasets().push_to_hub(\"argilla/ag_news_annotated\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset.to_hub(repo_id=\"argilla/ag_news_annotated\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset = rg.Dataset.from_hub(repo_id=\"argilla/ag_news_annotated\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "name": "Use your annotated dataset",
+   "provenance": []
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/course/fr/chapter11/section2.ipynb b/course/fr/chapter11/section2.ipynb
new file mode 100644
index 00000000..d3f33b61
--- /dev/null
+++ b/course/fr/chapter11/section2.ipynb
@@ -0,0 +1,5739 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "vZAvFVIAtFlq"
+   },
+   "source": [
+    "# Explorer les gabarits de chat avec SmolLM2\n",
+    "\n",
+    "Ce *notebook* montre comment utiliser les gabarits de chat avec le modèle `SmolLM2`. Les gabarits de chat permettent de structurer les interactions entre les utilisateurs et les modèles d'IA, en garantissant des réponses cohérentes et adaptées au contexte."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "K-lZu8JvtwUN",
+    "outputId": "c3871418-15bc-4265-ae8d-6d6036036d0e"
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c15d320002504d95bb86e87f50d43b08",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Installer les prérequis dans Google Colab\n",
+    "# !pip install transformers datasets trl huggingface_hub\n",
+    "\n",
+    "# S'authentifier sur Hugging Face\n",
+    "from huggingface_hub import login\n",
+    "\n",
+    "login()\n",
+    "\n",
+    "# Pour plus de facilité, vous pouvez créer une variable d'environnement contenant votre jeton de hub sous la forme HF_TOKEN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "tnHzBR7vtFlr"
+   },
+   "outputs": [],
+   "source": [
+    "# Importer les bibliothèques nécessaires\n",
+    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
+    "from trl import setup_chat_format\n",
+    "import torch"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "XTVOqbuetFlr"
+   },
+   "source": [
+    "## Gabarit de chat de SmolLM2\n",
+    "\n",
+    "Voyons comment utiliser un gabarit de chat avec le modèle `SmolLM2`. Nous allons définir une conversation simple et appliquer le gabarit de discussion."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 397,
+     "referenced_widgets": [
+      "b922b90106414644bc0e933f28dea1bf",
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+      "881b6196dfa0446e8c55a2420e484b6b",
+      "d54fb2da9f1f4a89ae962b8816314f43",
+      "77d3d81687e6417ab988b04984fc68f4",
+      "fbce0a69847e4099a55d1e39d4118c91",
+      "1513792bad534a0c9c381a131395c519",
+      "69f38fecf8ad403898634cfdfadf8925",
+      "17023310de9b4c3ebd8cc03758d59ef9",
+      "f3e23f781bce4429954d76bfea97aff4",
+      "530fc4c2bf1244628af7dea3e4b35cdf",
+      "96c2aae9198441569362135ad4bcbc98",
+      "76d306c21214412ab44e542d82e547aa",
+      "b9e41ef9e9c54fa7b71bc333604af74e"
+     ]
+    },
+    "id": "Nrxh0oX6tFls",
+    "outputId": "953e1527-8168-4346-9338-6e188ca31a1a"
+   },
+   "outputs": [],
+   "source": [
+    "# Définir dynamiquement l'appareil\n",
+    "device = (\n",
+    "    \"cuda\"\n",
+    "    if torch.cuda.is_available()\n",
+    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
+    ")\n",
+    "\n",
+    "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n",
+    "model = AutoModelForCausalLM.from_pretrained(\n",
+    "    pretrained_model_name_or_path=model_name\n",
+    ").to(device)\n",
+    "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)\n",
+    "model, tokenizer = setup_chat_format(model=model, tokenizer=tokenizer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "id": "zkJwILrbtFls"
+   },
+   "outputs": [],
+   "source": [
+    "# Définir les messages pour SmolLM2\n",
+    "messages = [\n",
+    "    {\"role\": \"user\", \"content\": \"Hello, how are you?\"},\n",
+    "    {\n",
+    "        \"role\": \"assistant\",\n",
+    "        \"content\": \"I'm doing well, thank you! How can I assist you today?\",\n",
+    "    },\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Ve4dgtjstFls"
+   },
+   "source": [
+    "# Appliquer le gabarit de chat sans tokenisation\n",
+    "\n",
+    "Le *tokenizer* représente la conversation sous la forme d'une chaîne avec des *tokens* spéciaux pour décrire le rôle de l'utilisateur et de l'assistant."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "pbAg-5x-tFls",
+    "outputId": "5f9482db-1fcf-4c13-ccaa-ef3f6eff7f76"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Conversation with template: <|im_start|>user\n",
+      "Hello, how are you?<|im_end|>\n",
+      "<|im_start|>assistant\n",
+      "I'm doing well, thank you! How can I assist you today?<|im_end|>\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "input_text = tokenizer.apply_chat_template(messages, tokenize=False)\n",
+    "\n",
+    "print(\"Conversation with template:\", input_text)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "sfvdglOqtFls"
+   },
+   "source": [
+    "# Décoder la conversation\n",
+    "\n",
+    "Notez que la conversation est représentée comme ci-dessus, mais avec un message d'assistant supplémentaire."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "mXUVdPeytFls",
+    "outputId": "80870e53-7bc1-426e-ac33-ba6748e030fc"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Conversation decoded: <|im_start|>user\n",
+      "Hello, how are you?<|im_end|>\n",
+      "<|im_start|>assistant\n",
+      "I'm doing well, thank you! How can I assist you today?<|im_end|>\n",
+      "<|im_start|>assistant\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "input_text = tokenizer.apply_chat_template(\n",
+    "    messages, tokenize=True, add_generation_prompt=True\n",
+    ")\n",
+    "\n",
+    "print(\"Conversation decoded:\", tokenizer.decode(token_ids=input_text))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "UcZQpspEtFlt"
+   },
+   "source": [
+    "# Tokeniser la conversation\n",
+    "\n",
+    "Bien sûr, le *tokenizer* tokenise également la conversation et le *token* spécial en tant qu'identifiants liés au vocabulaire du modèle.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "jc2PLxAMtFlt",
+    "outputId": "d2098780-b3f4-41ec-a1f3-b6da2b593c62"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Conversation tokenized: [1, 4093, 198, 19556, 28, 638, 359, 346, 47, 2, 198, 1, 520, 9531, 198, 57, 5248, 2567, 876, 28, 9984, 346, 17, 1073, 416, 339, 4237, 346, 1834, 47, 2, 198, 1, 520, 9531, 198]\n"
+     ]
+    }
+   ],
+   "source": [
+    "input_text = tokenizer.apply_chat_template(messages, add_generation_prompt=True)\n",
+    "\n",
+    "print(\"Conversation tokenized:\", input_text)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "m3eNp9a0tFlt"
+   },
+   "source": [
+    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
+    "    <h2 style='margin: 0;color:blue'>Exercice : Traitement d'un jeu de données pour faire un SFT</h2>\n",
+    "    <p>Prenez un jeu de données disponible sur le Hub d'Hugging Face et traitez-le pour pouvoir l'utiliser sur du SFT.</p>\n",
+    "    <p><b>Niveaux de difficulté</b></p>\n",
+    "    <p>🐢 Convertit le jeu de données `HuggingFaceTB/smoltalk` au format chatml.</p>\n",
+    "    <p>🐕 Convertit le jeu de données `openai/gsm8k` au format chatml.</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 381
+    },
+    "id": "qbkXV2_ItFlt",
+    "outputId": "06deadc3-2c63-4660-d2bd-05096ef07c9f"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<iframe\n",
+       "  src=\"https://huggingface.co/datasets/HuggingFaceTB/smoltalk/embed/viewer/all/train?row=0\"\n",
+       "  frameborder=\"0\"\n",
+       "  width=\"100%\"\n",
+       "  height=\"360px\"\n",
+       "></iframe>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.core.display import display, HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"<iframe\n",
+    "  src=\"https://huggingface.co/datasets/HuggingFaceTB/smoltalk/embed/viewer/all/train?row=0\"\n",
+    "  frameborder=\"0\"\n",
+    "  width=\"100%\"\n",
+    "  height=\"360px\"\n",
+    "></iframe>\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 241,
+     "referenced_widgets": [
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+      "55b14c03a41c495aacf8ac2d0f96ba0b"
+     ]
+    },
+    "id": "4p3atw4_tFlu",
+    "outputId": "62ee9812-3819-4a9c-9e24-5687368ffcd8"
+   },
+   "outputs": [],
+   "source": [
+    "from datasets import load_dataset\n",
+    "\n",
+    "ds = load_dataset(\"HuggingFaceTB/smoltalk\", \"everyday-conversations\")\n",
+    "\n",
+    "\n",
+    "def process_dataset(sample):\n",
+    "    # TODO : 🐢 Convertir l'échantillon en un format de chat\n",
+    "    # utiliser la méthode du tokenizer pour appliquer le gabarit de chat.\n",
+    "    return sample\n",
+    "\n",
+    "\n",
+    "ds = ds.map(process_dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 381
+    },
+    "id": "81fQeazltFlu",
+    "outputId": "36cf7148-9881-4f13-d0ce-76c82c4ab219"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<iframe\n",
+       "  src=\"https://huggingface.co/datasets/openai/gsm8k/embed/viewer/main/train\"\n",
+       "  frameborder=\"0\"\n",
+       "  width=\"100%\"\n",
+       "  height=\"360px\"\n",
+       "></iframe>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"<iframe\n",
+    "  src=\"https://huggingface.co/datasets/openai/gsm8k/embed/viewer/main/train\"\n",
+    "  frameborder=\"0\"\n",
+    "  width=\"100%\"\n",
+    "  height=\"360px\"\n",
+    "></iframe>\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "bWUSv7NMtFlu"
+   },
+   "outputs": [],
+   "source": [
+    "ds = load_dataset(\"openai/gsm8k\", \"main\")\n",
+    "\n",
+    "\n",
+    "def process_dataset(sample):\n",
+    "    # TODO : 🐕 Convertir l'échantillon en un format de chat\n",
+    "\n",
+    "    # 1. créer un format de message avec le rôle et le contenu\n",
+    "\n",
+    "    # 2. appliquer le gabarit de chat aux échantillons en utilisant la méthode du tokenizer.\n",
+    "\n",
+    "    return sample\n",
+    "\n",
+    "\n",
+    "ds = ds.map(process_dataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "qlXCuRKotFlu"
+   },
+   "source": [
+    "## Conclusion\n",
+    "\n",
+    "Ce *notebook* a présenté comment appliquer des gabarits de chat à différents modèles, `SmolLM2`. En structurant les interactions avec des gabarits de chat, nous pouvons nous assurer que les modèles d'IA fournissent des réponses cohérentes et contextuellement pertinentes.\n",
+    "\n",
+    "Dans l'exercice, vous avez essayé de convertir un jeu de données au format chatml. Heureusement, TRL le fait pour vous, mais il est utile de comprendre ce qui se passe sous le capot."
+   ]
+  }
+ ],
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diff --git a/course/fr/chapter11/section3.ipynb b/course/fr/chapter11/section3.ipynb
new file mode 100644
index 00000000..ec050e75
--- /dev/null
+++ b/course/fr/chapter11/section3.ipynb
@@ -0,0 +1,273 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Finetuning supervisé avec SFTTrainer\n",
+    "\n",
+    "Ce notebook montre comment finetuner le modèle `HuggingFaceTB/SmolLM2-135M` en utilisant le `SFTTrainer` de la bibliothèque `trl`. Les cellules du notebook s'exécutent et vont finetuner le modèle. Vous pouvez choisir votre difficulté en essayant différents jeux de données.\n",
+    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
+    "    <h2 style='margin: 0;color:blue'>Exercice : Finetuning de SmolLM2 avec SFTTrainer</h2>\n",
+    "    <p>Prenez un jeu de données provenant du Hub d'Hugging Face et finetuné un modèle sur dessus. </p> \n",
+    "    <p><b>Niveaux de difficulté</b></p>\n",
+    "    <p>🐢 Utilisez le jeu de données `HuggingFaceTB/smoltalk`</p>\n",
+    "    <p>🐕 Essayez le jeu de données `bigcode/the-stack-smol` et finetunez un modèle de génération de code sur un sous-ensemble spécifique `data/python`</p>\n",
+    "    <p>🦁 Sélectionnez un jeu de données en rapport avec un cas d'utilisation réel qui vous intéresse</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Installer les prérequis dans Google Colab\n",
+    "# !pip install transformers datasets trl huggingface_hub\n",
+    "\n",
+    "# S'authentifier sur Hugging Face\n",
+    "from huggingface_hub import login\n",
+    "\n",
+    "login()\n",
+    "\n",
+    "# Pour plus de facilité, vous pouvez créer une variable d'environnement contenant votre jeton de hub sous la forme HF_TOKEN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Importer les bibliothèques nécessaires\n",
+    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
+    "from datasets import load_dataset\n",
+    "from trl import SFTConfig, SFTTrainer, setup_chat_format\n",
+    "import torch\n",
+    "\n",
+    "device = (\n",
+    "    \"cuda\"\n",
+    "    if torch.cuda.is_available()\n",
+    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
+    ")\n",
+    "\n",
+    "# Charger le modèle et le tokenizer\n",
+    "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n",
+    "model = AutoModelForCausalLM.from_pretrained(\n",
+    "    pretrained_model_name_or_path=model_name\n",
+    ").to(device)\n",
+    "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)\n",
+    "\n",
+    "# Définir le format de chat\n",
+    "model, tokenizer = setup_chat_format(model=model, tokenizer=tokenizer)\n",
+    "\n",
+    "# Définir le nom du finetuning à sauvegarder et/ou à télécharger\n",
+    "finetune_name = \"SmolLM2-FT-MyDataset\"\n",
+    "finetune_tags = [\"smol-course\", \"module_1\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Générer avec le modèle de base\n",
+    "\n",
+    "Ici, nous allons essayer le modèle de base qui n'a pas de gabarit de chat. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Testons le modèle de base avant l'entraînement\n",
+    "prompt = \"Write a haiku about programming\"\n",
+    "\n",
+    "# Format avec gabarit\n",
+    "messages = [{\"role\": \"user\", \"content\": prompt}]\n",
+    "formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False)\n",
+    "\n",
+    "# Générer une réponse\n",
+    "inputs = tokenizer(formatted_prompt, return_tensors=\"pt\").to(device)\n",
+    "outputs = model.generate(**inputs, max_new_tokens=100)\n",
+    "print(\"Before training:\")\n",
+    "print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Préparation du jeu de données\n",
+    "\n",
+    "Nous allons charger un échantillon du jeu de données et le formater pour l'entraînement. Le jeu de données doit être structuré avec des paires entrée-sortie, où chaque entrée est une instruction et la sortie est la réponse attendue du modèle.\n",
+    "\n",
+    "**TRL va formater les messages d'entrée en se basant sur les gabarits de chat du modèle.** Ils doivent être représentés sous la forme d'une liste de dictionnaires avec les clés : `role` et `content`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Chargement d'un échantillon du jeu de données\n",
+    "from datasets import load_dataset\n",
+    "\n",
+    "# TODO : définir votre jeu de données et votre configuration en utilisant les paramètres path et name\n",
+    "ds = load_dataset(path=\"HuggingFaceTB/smoltalk\", name=\"everyday-conversations\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO : 🦁 Si votre jeu de données n'est pas dans un format que TRL peut convertir en gabarit de chat, vous devrez le traiter. Reportez-vous au [module](../chat_templates.md)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configurer le SFTTrainer\n",
+    "\n",
+    "Le `SFTTrainer` est configuré avec différents paramètres qui contrôlent le processus d'apprentissage. Ceux-ci incluent le nombre d'étapes d'entraînement, la taille de batch, le taux d'apprentissage et la stratégie d'évaluation. Ajustez ces paramètres en fonction de vos besoins spécifiques et de vos ressources de calcul."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Configurer le SFTTrainer\n",
+    "sft_config = SFTConfig(\n",
+    "    output_dir=\"./sft_output\",\n",
+    "    max_steps=1000,  # Ajuster en fonction de la taille du jeu de données et de la durée d'entraînement souhaitée\n",
+    "    per_device_train_batch_size=4,  # Régler en fonction de la capacité de mémoire de votre GPU\n",
+    "    learning_rate=5e-5,  # Point de départ commun pour le finetuning\n",
+    "    logging_steps=10,  # Fréquence d'enregistrement des métriques d'entraînement\n",
+    "    save_steps=100,  # Fréquence de sauvegarde des checkpoints du modèle\n",
+    "    evaluation_strategy=\"steps\",  # Évaluer le modèle à intervalles réguliers\n",
+    "    eval_steps=50,  # Fréquence de l'évaluation\n",
+    "    use_mps_device=(\n",
+    "        True if device == \"mps\" else False\n",
+    "    ),  # Utiliser MPS pour un entraînement à précision mixte\n",
+    "    hub_model_id=finetune_name,  # Définissez un nom unique pour votre modèle\n",
+    ")\n",
+    "\n",
+    "# Initialiser le SFTTrainer\n",
+    "trainer = SFTTrainer(\n",
+    "    model=model,\n",
+    "    args=sft_config,\n",
+    "    train_dataset=ds[\"train\"],\n",
+    "    tokenizer=tokenizer,\n",
+    "    eval_dataset=ds[\"test\"],\n",
+    ")\n",
+    "\n",
+    "# TODO : 🦁 🐕 aligner les paramètres de SFTTrainer avec le jeu de données que vous avez choisi. \n",
+    "# Par exemple, si vous utilisez le jeu de données `bigcode/the-stack-smol`, vous devrez choisir la colonne `content`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Entraînement du modèle\n",
+    "\n",
+    "Une fois le Trainer configuré, nous pouvons maintenant procéder à l'entraînement du modèle. Le processus d'entraînement consiste à itérer sur le jeu de données, à calculer la perte et à mettre à jour les paramètres du modèle afin de minimiser cette perte."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Entraîner le modèle\n",
+    "trainer.train()\n",
+    "\n",
+    "# Sauvegarder le modèle\n",
+    "trainer.save_model(f\"./{finetune_name}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trainer.push_to_hub(tags=finetune_tags)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
+    "    <h2 style='margin: 0;color:blue'>Exercice bonus : Générer avec un modèle finetuné</h2>\n",
+    "    <p>🐕 Utiliser le modèle finetuné pour générer une réponse, comme dans l'exemple de base</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Tester le modèle finetuné sur la même instruction\n",
+    "\n",
+    "# Testons le modèle de base avant l'entraînement\n",
+    "prompt = \"Write a haiku about programming\"\n",
+    "\n",
+    "# Format avec gabarit\n",
+    "messages = [{\"role\": \"user\", \"content\": prompt}]\n",
+    "formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False)\n",
+    "\n",
+    "# Générer une réponse\n",
+    "inputs = tokenizer(formatted_prompt, return_tensors=\"pt\").to(device)\n",
+    "\n",
+    "# TODO : utiliser le modèle finetuné pour générer une réponse, comme dans l'exemple de base."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 💐 Vous avez terminé !\n",
+    "\n",
+    "Ce *notebook* fournit un guide étape par étape pour finetuner le modèle `HuggingFaceTB/SmolLM2-135M` en utilisant le `SFTTrainer`. En suivant ces étapes, vous pouvez adapter le modèle pour effectuer des tâches spécifiques plus efficacement. Si vous voulez continuer à travailler sur ce cours, voici quelques étapes que vous pouvez essayer :\n",
+    "\n",
+    "- Essayez ce *notebook*  avec un niveau de difficulté plus élevé\n",
+    "- Examiner la PR d'un collègue\n",
+    "- Améliorez le matériel de cours par le biais d'une *issue* ou d'une PR."
+   ]
+  }
+ ],
+ "metadata": {
+  "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",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/course/fr/chapter11/section4.ipynb b/course/fr/chapter11/section4.ipynb
new file mode 100644
index 00000000..01fa16d3
--- /dev/null
+++ b/course/fr/chapter11/section4.ipynb
@@ -0,0 +1,513 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "z-6LLOPZouLg"
+   },
+   "source": [
+    "# Comment finetuner des LLMs avec des adaptateurs LoRA en utilisant Hugging Face TRL\n",
+    "\n",
+    "Ce *notebook* montre comment finetuner efficacement de grands modèles de langage en utilisant des adaptateurs LoRA (*Low-Rank Adaptation*). LoRA est une technique de finetuning efficace en termes de paramètres qui :\n",
+    "- gèle les poids du modèle pré-entraîné\n",
+    "- ajoute aux couches d'attention de petites matrices de décomposition de rangs entraînables\n",
+    "- Réduit généralement les paramètres entraînables d'environ 90%\n",
+    "- Maintient les performances du modèle tout en étant économe en mémoire\n",
+    "\n",
+    "Nous aborderons les points suivants\n",
+    "1. Mise en place de l'environnement de développement et configuration de LoRA\n",
+    "2. Créer et préparer le jeu de données pour l'entraînement de l'adaptateur\n",
+    "3. Finetuner en utilisant `trl` et `SFTTrainer` avec les adaptateurs LoRA\n",
+    "4. Tester le modèle et fusionner les adaptateurs (optionnel)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fXqd9BXgouLi"
+   },
+   "source": [
+    "## 1. Configurer l'environnement de développement\n",
+    "\n",
+    "Notre première étape consiste à installer les bibliothques d'Hugging Face et Pytorch, y compris trl, les transformers et les datasets. Si vous n'avez pas encore entendu parler de trl, ne vous inquiétez pas. Il s'agit d'une nouvelle bibliothèque au-dessus des transformers et des datasets permetant de finetuner, rlhf, aligner les LLMs ouverts plus facilement."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "tKvGVxImouLi"
+   },
+   "outputs": [],
+   "source": [
+    "# Installer les prérequis dans Google Colab\n",
+    "# !pip install transformers datasets trl huggingface_hub\n",
+    "\n",
+    "# S'authentifier sur Hugging Face\n",
+    "from huggingface_hub import login\n",
+    "\n",
+    "login()\n",
+    "\n",
+    "# Pour plus de facilité, vous pouvez créer une variable d'environnement contenant votre jeton de hub sous la forme HF_TOKEN"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "XHUzfwpKouLk"
+   },
+   "source": [
+    "## 2. Charger le jeu de données"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "id": "z4p6Bvo7ouLk"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatasetDict({\n",
+       "    train: Dataset({\n",
+       "        features: ['full_topic', 'messages'],\n",
+       "        num_rows: 2260\n",
+       "    })\n",
+       "    test: Dataset({\n",
+       "        features: ['full_topic', 'messages'],\n",
+       "        num_rows: 119\n",
+       "    })\n",
+       "})"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Charger un échantillon de jeu de données\n",
+    "from datasets import load_dataset\n",
+    "\n",
+    "# TODO : définir votre jeu de données et votre configuration en utilisant les paramètres path et name\n",
+    "dataset = load_dataset(path=\"HuggingFaceTB/smoltalk\", name=\"everyday-conversations\")\n",
+    "dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "9TOhJdtsouLk"
+   },
+   "source": [
+    "## 3. Fine-tune LLM using `trl` and the `SFTTrainer` with LoRA\n",
+    "\n",
+    "The [SFTTrainer](https://huggingface.co/docs/trl/sft_trainer) from `trl` provides integration with LoRA adapters through the [PEFT](https://huggingface.co/docs/peft/en/index) library. Key advantages of this setup include:\n",
+    "\n",
+    "1. **Memory Efficiency**: \n",
+    "   - Only adapter parameters are stored in GPU memory\n",
+    "   - Base model weights remain frozen and can be loaded in lower precision\n",
+    "   - Enables fine-tuning of large models on consumer GPUs\n",
+    "\n",
+    "2. **Training Features**:\n",
+    "   - Native PEFT/LoRA integration with minimal setup\n",
+    "   - Support for QLoRA (Quantized LoRA) for even better memory efficiency\n",
+    "\n",
+    "3. **Adapter Management**:\n",
+    "   - Adapter weight saving during checkpoints\n",
+    "   - Features to merge adapters back into base model\n",
+    "\n",
+    "We'll use LoRA in our example, which combines LoRA with 4-bit quantization to further reduce memory usage without sacrificing performance. The setup requires just a few configuration steps:\n",
+    "1. Define the LoRA configuration (rank, alpha, dropout)\n",
+    "2. Create the SFTTrainer with PEFT config\n",
+    "3. Train and save the adapter weights\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Importer les bibliothèques nécessaires\n",
+    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
+    "from datasets import load_dataset\n",
+    "from trl import SFTConfig, SFTTrainer, setup_chat_format\n",
+    "import torch\n",
+    "\n",
+    "device = (\n",
+    "    \"cuda\"\n",
+    "    if torch.cuda.is_available()\n",
+    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
+    ")\n",
+    "\n",
+    "# Charger le modèle et le tokenizer\n",
+    "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n",
+    "\n",
+    "model = AutoModelForCausalLM.from_pretrained(\n",
+    "    pretrained_model_name_or_path=model_name\n",
+    ").to(device)\n",
+    "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)\n",
+    "\n",
+    "# Définir le format de chat\n",
+    "model, tokenizer = setup_chat_format(model=model, tokenizer=tokenizer)\n",
+    "\n",
+    "# Définir le nom du finetuning à sauvegarder et/ou à télécharger\n",
+    "finetune_name = \"SmolLM2-FT-MyDataset\"\n",
+    "finetune_tags = [\"smol-course\", \"module_1\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ZbuVArTHouLk"
+   },
+   "source": [
+    "`SFTTrainer` supporte une intégration native avec `peft`, ce qui rend super facile le finetuning des LLMs en utilisant, par exemple, LoRA. Nous avons seulement besoin de créer notre `LoraConfig` et de le fournir au Trainer.\n",
+    "\n",
+    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
+    "    <h2 style='margin: 0;color:blue'>Exercice : Définir les paramètres de LoRA pour le finetuning</h2>\n",
+    "    <p>Prenez un jeu de données provenant du Hub d'Hugging Face et finetuné un modèle sur dessus. </p> \n",
+    "    <p><b>Niveaux de difficulté</b></p>\n",
+    "    <p>🐢 Utiliser les paramètres généraux pour un finetuning arbitraire</p>\n",
+    "    <p>🐕 Ajuster les paramètres et vérifier les poids et les biais</p>\n",
+    "    <p>🦁 Ajuster les paramètres et montrer les changements dans les résultats de l'inférence</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "blDSs9swouLk"
+   },
+   "outputs": [],
+   "source": [
+    "from peft import LoraConfig\n",
+    "\n",
+    "# TODO : Configurer les paramètres de LoRA\n",
+    "# r : dimension du rang des matrices LoRA (plus petite = plus de compression)\n",
+    "rank_dimension = 6\n",
+    "# lora_alpha : facteur d'échelle pour les couches LoRA (plus élevé = adaptation plus forte)\n",
+    "lora_alpha = 8\n",
+    "# lora_dropout : probabilité de dropout pour les couches LoRA (aide à prévenir le surentraînement)\n",
+    "lora_dropout = 0.05\n",
+    "\n",
+    "peft_config = LoraConfig(\n",
+    "    r=rank_dimension,  # Dimension du rang, généralement entre 4 et 32\n",
+    "    lora_alpha=lora_alpha,  # Facteur d'échelle LoRA, généralement 2x le rang\n",
+    "    lora_dropout=lora_dropout,  # Probabilité de dropout probability pour les couches de LoRA\n",
+    "    bias=\"none\",  # Type de biais pour le LoRA. Les biais correspondants seront mis à jour pendant l'entraînement\n",
+    "    target_modules=\"all-linear\",  # Modules auxquels appliquer le LoRA\n",
+    "    task_type=\"CAUSAL_LM\",  # Type de tâche pour l'architecture du modèle\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "l5NUDPcaouLl"
+   },
+   "source": [
+    "Avant de commencer notre entraînement, nous devons définir les hyperparamètres (`TrainingArguments`) que nous voulons utiliser."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "NqT28VZlouLl"
+   },
+   "outputs": [],
+   "source": [
+    "# Configuration de l'entraînement\n",
+    "# Hyperparamètres basés sur les recommandations du papier du QLoRA\n",
+    "args = SFTConfig(\n",
+    "    # Paramètres de sortie\n",
+    "    output_dir=finetune_name,  # Répertoire pour enregistrer les checkpoints du modèle\n",
+    "    # Durée de l'entraînement\n",
+    "    num_train_epochs=1,  # Nombre d'époques d'entraînement\n",
+    "    # Paramètres de la taille des batchs\n",
+    "    per_device_train_batch_size=2,  # Taille des batchs par GPU\n",
+    "    gradient_accumulation_steps=2,  # Accumuler les gradients pour obtenir un plus grand batch efficace\n",
+    "    # Optimisation de la mémoire\n",
+    "    gradient_checkpointing=True,  # Échanger le calcul contre des économies de mémoire\n",
+    "    # Paramètres de l'optimiseur\n",
+    "    optim=\"adamw_torch_fused\",  # Utiliser AdamW fusionné pour plus d'efficacité\n",
+    "    learning_rate=2e-4,  # Taux d'apprentissage (papier du QLoRA)\n",
+    "    max_grad_norm=0.3,  # Seuil d'écrêtage du gradient\n",
+    "    # Taux d'apprentissage\n",
+    "    warmup_ratio=0.03,  # Portion de pas pour l'échauffement\n",
+    "    lr_scheduler_type=\"constant\",  # Maintenir un rythme d'apprentissage constant après l'échauffement\n",
+    "    # Enregistrement et sauvegarde\n",
+    "    logging_steps=10,  # Enregistrement des métriques tous les N pas\n",
+    "    save_strategy=\"epoch\",  # Sauvegarde du checkpoint à chaque époque\n",
+    "    # Paramètres de précision\n",
+    "    bf16=True,  # Utiliser la précision bfloat16\n",
+    "    # Paramètres d'intégration\n",
+    "    push_to_hub=False,  # Ne pas pousser vers le Hub\n",
+    "    report_to=\"none\",  # Désactiver l'enregistrement externe\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "cGhR7uFBouLl"
+   },
+   "source": [
+    "Nous avons maintenant tous les éléments nécessaires pour créer notre `SFTTrainer` et commencer à entraîner notre modèle."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "M00Har2douLl"
+   },
+   "outputs": [],
+   "source": [
+    "max_seq_length = 1512  # longueur maximale des séquences pour le modèle et le paquetage du jeu de données\n",
+    "\n",
+    "# Créer SFTTrainer avec la configuration LoRA\n",
+    "trainer = SFTTrainer(\n",
+    "    model=model,\n",
+    "    args=args,\n",
+    "    train_dataset=dataset[\"train\"],\n",
+    "    peft_config=peft_config,  # Configuration LoRA\n",
+    "    max_seq_length=max_seq_length,  # Longueur maximale de la séquence\n",
+    "    tokenizer=tokenizer,\n",
+    "    packing=True,  # Activer l'emballage d'entrée pour plus d'efficacité\n",
+    "    dataset_kwargs={\n",
+    "        \"add_special_tokens\": False,  # Tokens spéciaux gérés par le gabarit\n",
+    "        \"append_concat_token\": False,  # Aucun séparateur supplémentaire n'est nécessaire\n",
+    "    },\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zQ_kRN24ouLl"
+   },
+   "source": [
+    "Commencez à entraîner notre modèle en appelant la méthode `train()` sur notre instance `Trainer`. Cela va démarrer la boucle d'entraînement et entraîner notre modèle pendant 3 époques. Puisque nous utilisons une méthode PEFT, nous ne sauvegarderons que les poids du modèle adapté et non le modèle complet."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Tq4nIYqKouLl"
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "300e5dfbb4b54750b77324345c7591f9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/72 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "TrainOutput(global_step=72, training_loss=1.6402628521124523, metrics={'train_runtime': 195.2398, 'train_samples_per_second': 1.485, 'train_steps_per_second': 0.369, 'total_flos': 282267289092096.0, 'train_loss': 1.6402628521124523, 'epoch': 0.993103448275862})"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# commencer l'entraînement, le modèle sera automatiquement sauvegardé sur le Hub et dans le répertoire de sortie.\n",
+    "trainer.train()\n",
+    "\n",
+    "# Sauvegarder le modèle\n",
+    "trainer.save_model()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "y4HHSYYzouLl"
+   },
+   "source": [
+    "L'entraînement avec Flash Attention pour 3 époques avec un jeu de données de 15k échantillons a pris 4:14:36 sur un `g5.2xlarge`. L'instance coûte `1.21$/h` ce qui nous amène à un coût total de seulement ~`5.3$`.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "C309KsXjouLl"
+   },
+   "source": [
+    "### Fusionner l'adaptateur LoRA dans le modèle original\n",
+    "\n",
+    "Lors de l'utilisation de LoRA, nous n'entraînons que les poids de l'adaptateur tout en gardant le modèle de base gelé. Pendant l'entraînement, nous sauvegardons uniquement ces poids d'adaptateur légers (~2-10MB) plutôt qu'une copie complète du modèle. Cependant, pour le déploiement, vous pouvez vouloir fusionner les adaptateurs dans le modèle de base pour :\n",
+    "\n",
+    "1. **Déploiement simplifié** : Fichier de modèle unique au lieu du modèle de base + adaptateurs\n",
+    "2. **Vitesse d'inférence** : Pas de surcharge de calcul des adaptateurs\n",
+    "3. **Compatibilité avec les frameworks** : Meilleure compatibilité avec les frameworks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from peft import AutoPeftModelForCausalLM\n",
+    "\n",
+    "\n",
+    "# Chargement du modèle PEFT sur le CPU\n",
+    "model = AutoPeftModelForCausalLM.from_pretrained(\n",
+    "    pretrained_model_name_or_path=args.output_dir,\n",
+    "    torch_dtype=torch.float16,\n",
+    "    low_cpu_mem_usage=True,\n",
+    ")\n",
+    "\n",
+    "# Fusionner le modèle LoRA et le modèle de base et sauvegarder\n",
+    "merged_model = model.merge_and_unload()\n",
+    "merged_model.save_pretrained(\n",
+    "    args.output_dir, safe_serialization=True, max_shard_size=\"2GB\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "-yO6E9quouLl"
+   },
+   "source": [
+    "## 3. Tester le modèle et exécuter l'inférence\n",
+    "\n",
+    "Une fois l'entraînement terminé, nous voulons tester notre modèle. Nous allons charger différents échantillons du jeu de données original et évaluer le modèle sur ces échantillons, en utilisant une boucle simple et l'*accuracy* comme métrique.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
+    "    <h2 style='margin: 0;color:blue'>Exercice bonus : Chargement de l'adaptateur LoRA</h2>\n",
+    "    <p>Utilisez ce que vous avez appris dans le notebook pour charger votre adaptateur LoRA entraîné pour l'inférence</p> \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "id": "I5B494OdouLl"
+   },
+   "outputs": [],
+   "source": [
+    "# libérer la mémoire\n",
+    "del model\n",
+    "del trainer\n",
+    "torch.cuda.empty_cache()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "P1UhohVdouLl"
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from peft import AutoPeftModelForCausalLM\n",
+    "from transformers import AutoTokenizer, pipeline\n",
+    "\n",
+    "# Chargement du modèle avec l'adaptateur PEFT\n",
+    "tokenizer = AutoTokenizer.from_pretrained(finetune_name)\n",
+    "model = AutoPeftModelForCausalLM.from_pretrained(\n",
+    "    finetune_name, device_map=\"auto\", torch_dtype=torch.float16\n",
+    ")\n",
+    "pipe = pipeline(\n",
+    "    \"text-generation\", model=merged_model, tokenizer=tokenizer, device=device\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "99uFDAuuouLl"
+   },
+   "source": [
+    "Testons quelques échantillons d'instructions et voyons comment le modèle se comporte."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "id": "-shSmUbvouLl",
+    "outputId": "16d97c61-3b31-4040-c780-3c4de75c3824"
+   },
+   "outputs": [],
+   "source": [
+    "prompts = [\n",
+    "    \"What is the capital of Germany? Explain why thats the case and if it was different in the past?\",\n",
+    "    \"Write a Python function to calculate the factorial of a number.\",\n",
+    "    \"A rectangular garden has a length of 25 feet and a width of 15 feet. If you want to build a fence around the entire garden, how many feet of fencing will you need?\",\n",
+    "    \"What is the difference between a fruit and a vegetable? Give examples of each.\",\n",
+    "]\n",
+    "\n",
+    "\n",
+    "def test_inference(prompt):\n",
+    "    prompt = pipe.tokenizer.apply_chat_template(\n",
+    "        [{\"role\": \"user\", \"content\": prompt}],\n",
+    "        tokenize=False,\n",
+    "        add_generation_prompt=True,\n",
+    "    )\n",
+    "    outputs = pipe(\n",
+    "        prompt,\n",
+    "    )\n",
+    "    return outputs[0][\"generated_text\"][len(prompt) :].strip()\n",
+    "\n",
+    "\n",
+    "for prompt in prompts:\n",
+    "    print(f\"    prompt:\\n{prompt}\")\n",
+    "    print(f\"    response:\\n{test_inference(prompt)}\")\n",
+    "    print(\"-\" * 50)"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "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",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}