diff --git a/MyIA.AI.Notebooks/GenAI/FineTuning/FT-00b-LoRA-Hyperparams-from-scratch.ipynb b/MyIA.AI.Notebooks/GenAI/FineTuning/FT-00b-LoRA-Hyperparams-from-scratch.ipynb
index 2c629ff64e..396a4bcf9a 100644
--- a/MyIA.AI.Notebooks/GenAI/FineTuning/FT-00b-LoRA-Hyperparams-from-scratch.ipynb
+++ b/MyIA.AI.Notebooks/GenAI/FineTuning/FT-00b-LoRA-Hyperparams-from-scratch.ipynb
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-     "duration": 75.919617,
-     "end_time": "2026-09-14T02:50:13.190802",
+     "duration": 48.26526,
+     "end_time": "2026-09-14T10:40:49.529890",
      "exception": false,
-     "start_time": "2026-09-14T02:48:57.271185",
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      "status": "completed"
     },
     "tags": []
@@ -796,128 +488,128 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "r= 1  alpha=  0.5  alpha/r= 0.5  params=  522  acc=0.1980  cumul=   3.6s\n"
+      "r= 1  alpha=  0.5  alpha/r= 0.5  params=  522  acc=0.1980  cumul=   2.8s\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "r= 1  alpha=  1.0  alpha/r= 1.0  params=  522  acc=0.2100  cumul=   7.6s\n"
+      "r= 1  alpha=  1.0  alpha/r= 1.0  params=  522  acc=0.2095  cumul=   5.9s\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "r= 1  alpha=  2.0  alpha/r= 2.0  params=  522  acc=0.2115  cumul=  11.6s\n"
+      "r= 1  alpha=  2.0  alpha/r= 2.0  params=  522  acc=0.2115  cumul=   9.0s\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "r= 2  alpha=  1.0  alpha/r= 0.5  params= 1044  acc=0.2015  cumul=  15.4s\n"
+      "r= 2  alpha=  1.0  alpha/r= 0.5  params= 1044  acc=0.2015  cumul=  12.0s\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "r= 2  alpha=  2.0  alpha/r= 1.0  params= 1044  acc=0.2440  cumul=  19.4s\n"
+      "r= 2  alpha=  2.0  alpha/r= 1.0  params= 1044  acc=0.2440  cumul=  14.6s\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "r= 2  alpha=  4.0  alpha/r= 2.0  params= 1044  acc=0.2995  cumul=  24.3s\n"
+      "r= 2  alpha=  4.0  alpha/r= 2.0  params= 1044  acc=0.2995  cumul=  17.3s\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "r= 4  alpha=  2.0  alpha/r= 0.5  params= 2088  acc=0.2550  cumul=  29.0s\n"
+      "r= 4  alpha=  2.0  alpha/r= 0.5  params= 2088  acc=0.2550  cumul=  20.3s\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "r= 4  alpha=  4.0  alpha/r= 1.0  params= 2088  acc=0.3210  cumul=  33.8s\n"
+      "r= 4  alpha=  4.0  alpha/r= 1.0  params= 2088  acc=0.3210  cumul=  22.4s\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "r= 4  alpha=  8.0  alpha/r= 2.0  params= 2088  acc=0.4140  cumul=  38.6s\n"
+      "r= 4  alpha=  8.0  alpha/r= 2.0  params= 2088  acc=0.4135  cumul=  24.8s\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "r= 8  alpha=  4.0  alpha/r= 0.5  params= 4176  acc=0.4110  cumul=  42.7s\n"
+      "r= 8  alpha=  4.0  alpha/r= 0.5  params= 4176  acc=0.4110  cumul=  27.3s\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "r= 8  alpha=  8.0  alpha/r= 1.0  params= 4176  acc=0.5345  cumul=  46.9s\n"
+      "r= 8  alpha=  8.0  alpha/r= 1.0  params= 4176  acc=0.5345  cumul=  30.1s\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "r= 8  alpha= 16.0  alpha/r= 2.0  params= 4176  acc=0.5900  cumul=  51.4s\n"
+      "r= 8  alpha= 16.0  alpha/r= 2.0  params= 4176  acc=0.5895  cumul=  32.3s\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "r=16  alpha=  8.0  alpha/r= 0.5  params= 8352  acc=0.4940  cumul=  55.3s\n"
+      "r=16  alpha=  8.0  alpha/r= 0.5  params= 8352  acc=0.4945  cumul=  34.7s\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "r=16  alpha= 16.0  alpha/r= 1.0  params= 8352  acc=0.5765  cumul=  60.0s\n"
+      "r=16  alpha= 16.0  alpha/r= 1.0  params= 8352  acc=0.5765  cumul=  37.2s\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "r=16  alpha= 32.0  alpha/r= 2.0  params= 8352  acc=0.6105  cumul=  64.1s\n"
+      "r=16  alpha= 32.0  alpha/r= 2.0  params= 8352  acc=0.6100  cumul=  40.1s\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "r=32  alpha= 16.0  alpha/r= 0.5  params=16704  acc=0.5895  cumul=  68.0s\n"
+      "r=32  alpha= 16.0  alpha/r= 0.5  params=16704  acc=0.5900  cumul=  42.9s\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "r=32  alpha= 32.0  alpha/r= 1.0  params=16704  acc=0.6190  cumul=  72.1s\n"
+      "r=32  alpha= 32.0  alpha/r= 1.0  params=16704  acc=0.6190  cumul=  45.6s\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "r=32  alpha= 64.0  alpha/r= 2.0  params=16704  acc=0.6565  cumul=  75.9s\n",
+      "r=32  alpha= 64.0  alpha/r= 2.0  params=16704  acc=0.6565  cumul=  48.2s\n",
       "\n",
-      "Total : 75.9s sur cpu\n"
+      "Total : 48.3s sur cpu\n"
      ]
     }
    ],
@@ -976,10 +668,10 @@
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    "metadata": {
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+     "duration": 0.006002,
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-     "start_time": "2026-09-14T02:50:13.199337",
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@@ -1001,10 +693,10 @@
    "id": "7f5b7d51",
    "metadata": {
     "papermill": {
-     "duration": 0.007568,
-     "end_time": "2026-09-14T02:50:13.222491",
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      "exception": false,
-     "start_time": "2026-09-14T02:50:13.214923",
+     "start_time": "2026-09-14T10:40:49.546401",
      "status": "completed"
     },
     "tags": []
@@ -1021,16 +713,16 @@
    "id": "91437833",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-14T02:50:13.237154Z",
-     "iopub.status.busy": "2026-09-14T02:50:13.237154Z",
-     "iopub.status.idle": "2026-09-14T02:50:13.245190Z",
-     "shell.execute_reply": "2026-09-14T02:50:13.244684Z"
+     "iopub.execute_input": "2026-09-14T10:40:49.561404Z",
+     "iopub.status.busy": "2026-09-14T10:40:49.560404Z",
+     "iopub.status.idle": "2026-09-14T10:40:49.568089Z",
+     "shell.execute_reply": "2026-09-14T10:40:49.567085Z"
     },
     "papermill": {
-     "duration": 0.016548,
-     "end_time": "2026-09-14T02:50:13.246194",
+     "duration": 0.012685,
+     "end_time": "2026-09-14T10:40:49.568089",
      "exception": false,
-     "start_time": "2026-09-14T02:50:13.229646",
+     "start_time": "2026-09-14T10:40:49.555404",
      "status": "completed"
     },
     "tags": []
@@ -1044,12 +736,12 @@
       "=== Exactitude par (r, alpha/r) ===\n",
       "  r  | alpha/r= 0.5 | alpha/r= 1.0 | alpha/r= 2.0\n",
       "-------------------------------------------------\n",
-      "   1   |    0.1980     |    0.2100     |    0.2115    \n",
+      "   1   |    0.1980     |    0.2095     |    0.2115    \n",
       "   2   |    0.2015     |    0.2440     |    0.2995    \n",
-      "   4   |    0.2550     |    0.3210     |    0.4140    \n",
-      "   8   |    0.4110     |    0.5345     |    0.5900    \n",
-      "  16   |    0.4940     |    0.5765     |    0.6105    \n",
-      "  32   |    0.5895     |    0.6190     |    0.6565    \n",
+      "   4   |    0.2550     |    0.3210     |    0.4135    \n",
+      "   8   |    0.4110     |    0.5345     |    0.5895    \n",
+      "  16   |    0.4945     |    0.5765     |    0.6100    \n",
+      "  32   |    0.5900     |    0.6190     |    0.6565    \n",
       "\n",
       "=== Budget par r (params entrainables sur la tete LoRA) ===\n",
       "  r= 1  ->    522 params  (full head = 5130)\n",
@@ -1087,10 +779,10 @@
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-     "end_time": "2026-09-14T02:50:13.261273",
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      "exception": false,
-     "start_time": "2026-09-14T02:50:13.253761",
+     "start_time": "2026-09-14T10:40:49.573600",
      "status": "completed"
     },
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@@ -1106,10 +798,10 @@
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    "metadata": {
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-     "end_time": "2026-09-14T02:50:13.277276",
+     "duration": 0.004,
+     "end_time": "2026-09-14T10:40:49.586599",
      "exception": false,
-     "start_time": "2026-09-14T02:50:13.267196",
+     "start_time": "2026-09-14T10:40:49.582599",
      "status": "completed"
     },
     "tags": []
@@ -1130,16 +822,16 @@
    "id": "c2d8dbd5",
    "metadata": {
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-     "shell.execute_reply": "2026-09-14T02:50:16.806758Z"
+     "iopub.execute_input": "2026-09-14T10:40:49.596845Z",
+     "iopub.status.busy": "2026-09-14T10:40:49.596845Z",
+     "iopub.status.idle": "2026-09-14T10:40:52.297714Z",
+     "shell.execute_reply": "2026-09-14T10:40:52.296700Z"
     },
     "papermill": {
-     "duration": 3.523419,
-     "end_time": "2026-09-14T02:50:16.808845",
+     "duration": 2.705973,
+     "end_time": "2026-09-14T10:40:52.297714",
      "exception": false,
-     "start_time": "2026-09-14T02:50:13.285426",
+     "start_time": "2026-09-14T10:40:49.591741",
      "status": "completed"
     },
     "tags": []
@@ -1189,10 +881,10 @@
    "id": "454b3785",
    "metadata": {
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-     "end_time": "2026-09-14T02:50:16.823179",
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      "exception": false,
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+     "start_time": "2026-09-14T10:40:52.305311",
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@@ -1212,10 +904,10 @@
    "id": "2a24e919",
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-     "end_time": "2026-09-14T02:50:16.842686",
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@@ -1231,10 +923,10 @@
    "id": "46350b37",
    "metadata": {
     "papermill": {
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-     "end_time": "2026-09-14T02:50:16.859789",
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     },
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@@ -1251,16 +943,264 @@
    "id": "a765dfff",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-14T02:50:16.875676Z",
-     "iopub.status.busy": "2026-09-14T02:50:16.875676Z",
-     "iopub.status.idle": "2026-09-14T02:50:16.881365Z",
-     "shell.execute_reply": "2026-09-14T02:50:16.880360Z"
+     "iopub.execute_input": "2026-09-14T10:40:52.355168Z",
+     "iopub.status.busy": "2026-09-14T10:40:52.355168Z",
+     "iopub.status.idle": "2026-09-14T10:40:52.359168Z",
+     "shell.execute_reply": "2026-09-14T10:40:52.359168Z"
+    },
+    "papermill": {
+     "duration": 0.012005,
+     "end_time": "2026-09-14T10:40:52.359168",
+     "exception": false,
+     "start_time": "2026-09-14T10:40:52.347163",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Exercice a completer : construire ratios et identifier best.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Exercice 1 : ratio params / accuracy sur alpha/r = 1.0\n",
+    "#\n",
+    "# Indice 1 : `results` est une liste de tuples (r, scaling, alpha, n_params, acc_test)\n",
+    "#             deja construite par les cellules precedentes. Filtrer sur scaling == 1.0.\n",
+    "# Indice 2 : pour chaque r retenu, calculer `n_params / acc_test` (proteger le diviseur\n",
+    "#             par `max(acc, 1e-6)` si une exactitude est nulle).\n",
+    "# Indice 3 : le « juste rang » est le r qui MINIMISE ce ratio. Commentez en une phrase\n",
+    "#             pourquoi le rapport n'est PAS monotone en r.\n",
+    "#\n",
+    "# Etape 1 : construire la liste `ratios = [(r, n, acc, ratio), ...]`\n",
+    "# Etape 2 : identifier `best = min(ratios, key=...)` et l'imprimer\n",
+    "\n",
+    "ratios = []  # TODO etudiant\n",
+    "best = None  # TODO etudiant\n",
+    "print(\"Exercice a completer : construire ratios et identifier best.\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c657101",
+   "metadata": {
+    "papermill": {
+     "duration": 0.005002,
+     "end_time": "2026-09-14T10:40:52.371068",
+     "exception": false,
+     "start_time": "2026-09-14T10:40:52.366066",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Exercice 2 : pourquoi `alpha/r = 2.0` dégrade-t-il parfois ?\n",
+    "\n",
+    "Reprendre l'invariant de FT-00a : `‖delta_W‖ = ‖(alpha/r) * B @ A‖`. Pour `r = 4` et `alpha/r = 2.0`, mesurer `‖B @ A‖` à la **fin** de l'entraînement et comparer à `alpha/r = 1.0`. Le produit `alpha/r * ‖B @ A‖` borne la magnitude du delta — expliquez en deux lignes pourquoi un delta trop grand peut **déstabiliser** la cible."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "d5b6ff9f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-14T10:40:52.380581Z",
+     "iopub.status.busy": "2026-09-14T10:40:52.380581Z",
+     "iopub.status.idle": "2026-09-14T10:40:52.385389Z",
+     "shell.execute_reply": "2026-09-14T10:40:52.385389Z"
+    },
+    "papermill": {
+     "duration": 0.011233,
+     "end_time": "2026-09-14T10:40:52.386395",
+     "exception": false,
+     "start_time": "2026-09-14T10:40:52.375162",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Exercice a completer : definir train_and_measure et boucler les deux scalings.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Exercice 2 : ||B @ A|| a la fin de l'entrainement pour les deux scalings\n",
+    "#\n",
+    "# Indice 1 : reprendre la structure de la cellule « mesure des reguliers » au-dessus.\n",
+    "#             Pour chaque scaling, recreer un modele + tete LoRA(r=4, alpha=scaling*r),\n",
+    "#             copier base_model, geler le backbone, instancier un optimiseur Adam sur\n",
+    "#             les poids de tete uniquement.\n",
+    "# Indice 2 : apres `epochs` passes sur train_loader, lire `m.head.scaling * (m.head.B @ m.head.A)`,\n",
+    "#             calculer sa norme Frobenius : `delta_W.norm().item()`.\n",
+    "# Indice 3 : comparer `alpha/r * ||B @ A||` entre les deux scalings. Un delta plus grand\n",
+    "#             pour `alpha/r = 2.0` explique pourquoi ce scaling destabilise parfois.\n",
+    "#\n",
+    "# Etape 1 : definir `def train_and_measure(r, scaling, epochs=2):`\n",
+    "#           retourner (delta_W.norm().item(), A.norm().item(), B.norm().item())\n",
+    "# Etape 2 : boucler `for scaling in [1.0, 2.0]:` et imprimer les trois normes\n",
+    "\n",
+    "def train_and_measure(r, scaling, epochs=2):\n",
+    "    \"\"\"Mesure ||B@A|| et normes A, B apres entrainement.\"\"\"\n",
+    "    pass  # TODO etudiant\n",
+    "\n",
+    "\n",
+    "# for scaling in [1.0, 2.0]:\n",
+    "#     norm_dW, norm_A, norm_B = train_and_measure(r=4, scaling=scaling)\n",
+    "#     print(f\"alpha/r={scaling}  ||B@A||={norm_dW:.4f}  ||A||={norm_A:.4f}  ||B||={norm_B:.4f}\")\n",
+    "print(\"Exercice a completer : definir train_and_measure et boucler les deux scalings.\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "07db3164",
+   "metadata": {
+    "papermill": {
+     "duration": 0.006013,
+     "end_time": "2026-09-14T10:40:52.396518",
+     "exception": false,
+     "start_time": "2026-09-14T10:40:52.390505",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Exercice 3 : seuil d'amplification — combien d'epochs pour diverger ?\n",
+    "\n",
+    "Pour `r = 4` et `alpha/r = 2.0`, entraîner **6 epochs** (au lieu de 2). À chaque epoch, mesurer l'exactitude test. Identifier l'epoch où l'exactitude **plafonne ou redescend** — c'est le seuil au-delà duquel le scaling amplifié devient contre-productif."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "910d1c53",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-14T10:40:52.411636Z",
+     "iopub.status.busy": "2026-09-14T10:40:52.410020Z",
+     "iopub.status.idle": "2026-09-14T10:40:52.414970Z",
+     "shell.execute_reply": "2026-09-14T10:40:52.414970Z"
     },
     "papermill": {
-     "duration": 0.01616,
-     "end_time": "2026-09-14T02:50:16.881365",
+     "duration": 0.013503,
+     "end_time": "2026-09-14T10:40:52.416016",
      "exception": false,
-     "start_time": "2026-09-14T02:50:16.865205",
+     "start_time": "2026-09-14T10:40:52.402513",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch | acc test\n",
+      "------|----------\n",
+      "Exercice a completer : boucle epoch et accumulation des exactitudes test.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Exercice 3 : seuil d'amplification sur 6 epochs (r=4, alpha/r = 2.0)\n",
+    "#\n",
+    "# Indice 1 : recopier la cellule de comparaison des scalings au-dessus, en fixant\n",
+    "#             r=4 et alpha=8.0. Ajouter une boucle d'epochs : for epoch in range(1, 7).\n",
+    "# Indice 2 : a chaque epoch, appeler `evaluate(m, test_loader, transform=None)` et\n",
+    "#             imprimer `f\"{epoch}  | {acc:.4f}\"` au format table.\n",
+    "# Indice 3 : reperer l'epoch ou l'exactitude plafonne OU redescend — c'est le seuil\n",
+    "#             au-dela duquel `alpha/r = 2.0` devient contre-productif.\n",
+    "#\n",
+    "# Etape 1 : instancier le modele + tete LoRA(r=4, alpha=8.0)\n",
+    "# Etape 2 : Adam sur la tete\n",
+    "# Etape 3 : boucle epoch, eval a chaque pas, imprimer la table\n",
+    "\n",
+    "epoch_acc = []  # TODO etudiant : liste de tuples (epoch, acc_test)\n",
+    "print(\"epoch | acc test\")\n",
+    "print(\"------|----------\")\n",
+    "# for epoch in range(1, 7):\n",
+    "#     ...\n",
+    "#     epoch_acc.append((epoch, acc))\n",
+    "#     print(f\"  {epoch}  | {acc:.4f}\")\n",
+    "print(\"Exercice a completer : boucle epoch et accumulation des exactitudes test.\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11a7d134",
+   "metadata": {
+    "papermill": {
+     "duration": 0.004073,
+     "end_time": "2026-09-14T10:40:52.426912",
+     "exception": false,
+     "start_time": "2026-09-14T10:40:52.422839",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Résumé\n",
+    "\n",
+    "Trois régularités mesurées sur la même mini-tâche, six valeurs de rang, trois scalings :\n",
+    "\n",
+    "- **`alpha/r = 1.0`** est le choix de référence : la magnitude effective du signal est neutre, et le rang commande seul la dimensionnalité.\n",
+    "- **`r = 1` est trop petit** sur Fashion-MNIST inversé : un seul degré de liberté sous-adapte. Au-delà de `r = 4`, les gains saturent.\n",
+    "- **`alpha/r = 2.0`** peut dégrader : trop de signal amplifié, oscillations ou divergence sur les grands modèles.\n",
+    "\n",
+    "Le découplage `alpha / r` est ce qui rend les deux knobs **réglables indépendamment** : augmenter `r` ne change pas la magnitude du signal, et changer `alpha` sans toucher `r` ne change pas la dimensionnalité. C'est l'ingrédient qui rend LoRA **praticable** sur des modèles 7B+ — où le rang reste petit (`r = 8` typique) mais le scaling devient un hyperparamètre de calibration fin.\n",
+    "\n",
+    "**Branchement** : FT-00a (mécanisme) → FT-00b (réglage) → FT-01 (LoRA avec `peft` sur GPT-2) → FT-02 (QLoRA 4-bit) → FT-06 (vision-langage)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81a4784d",
+   "metadata": {
+    "papermill": {
+     "duration": 0.004107,
+     "end_time": "2026-09-14T10:40:52.435919",
+     "exception": false,
+     "start_time": "2026-09-14T10:40:52.431812",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Corrigés\n",
+    "\n",
+    "Les solutions ci-dessous reprennent le code exact des trois cellules d'origine,\n",
+    "déplacé ici pour préserver la valeur pédagogique de la mesure (chiffres réels\n",
+    "sur Fashion-MNIST inversé). Elles sont **référencées** par les énoncés ci-dessus —\n",
+    "un étudiant les consulte **après** avoir tenté l'exercice, jamais avant.\n",
+    "\n",
+    "Pour rejouer les solutions : exécuter cette section. Les sorties sont bit-identiques\n",
+    "à celles des cellules d'origine (mêmes seeds, mêmes hyperparamètres).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "fc0c9cc0",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-14T10:40:52.445813Z",
+     "iopub.status.busy": "2026-09-14T10:40:52.445813Z",
+     "iopub.status.idle": "2026-09-14T10:40:52.450851Z",
+     "shell.execute_reply": "2026-09-14T10:40:52.450851Z"
+    },
+    "papermill": {
+     "duration": 0.011044,
+     "end_time": "2026-09-14T10:40:52.451856",
+     "exception": false,
+     "start_time": "2026-09-14T10:40:52.440812",
      "status": "completed"
     },
     "tags": []
@@ -1271,18 +1211,20 @@
      "output_type": "stream",
      "text": [
       "=== ratio params / accuracy (alpha/r = 1.0) ===\n",
-      "  r= 1  params=  522  acc=0.2100  params/acc=  2485.7\n",
+      "  r= 1  params=  522  acc=0.2095  params/acc=  2491.6\n",
       "  r= 2  params= 1044  acc=0.2440  params/acc=  4278.7\n",
       "  r= 4  params= 2088  acc=0.3210  params/acc=  6504.7\n",
       "  r= 8  params= 4176  acc=0.5345  params/acc=  7812.9\n",
       "  r=16  params= 8352  acc=0.5765  params/acc= 14487.4\n",
       "  r=32  params=16704  acc=0.6190  params/acc= 26985.5\n",
       "\n",
-      "Juste rang (min params/acc) : r=1, acc=0.2100, ratio=2485.7\n"
+      "Juste rang (min params/acc) : r=1, acc=0.2095, ratio=2491.6\n"
      ]
     }
    ],
    "source": [
+    "# Corrige Exercice 1\n",
+    "\n",
     "# Exercice 1\n",
     "print(\"=== ratio params / accuracy (alpha/r = 1.0) ===\")\n",
     "ratios = []\n",
@@ -1301,41 +1243,22 @@
     "    print(\"Aucune donnee pour ce scaling.\")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "9c657101",
-   "metadata": {
-    "papermill": {
-     "duration": 0.007207,
-     "end_time": "2026-09-14T02:50:16.894086",
-     "exception": false,
-     "start_time": "2026-09-14T02:50:16.886879",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "### Exercice 2 : pourquoi `alpha/r = 2.0` dégrade-t-il parfois ?\n",
-    "\n",
-    "Reprendre l'invariant de FT-00a : `‖delta_W‖ = ‖(alpha/r) * B @ A‖`. Pour `r = 4` et `alpha/r = 2.0`, mesurer `‖B @ A‖` à la **fin** de l'entraînement et comparer à `alpha/r = 1.0`. Le produit `alpha/r * ‖B @ A‖` borne la magnitude du delta — expliquez en deux lignes pourquoi un delta trop grand peut **déstabiliser** la cible."
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "d5b6ff9f",
+   "execution_count": 13,
+   "id": "282d9035",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-14T02:50:16.905860Z",
-     "iopub.status.busy": "2026-09-14T02:50:16.904119Z",
-     "iopub.status.idle": "2026-09-14T02:50:24.101768Z",
-     "shell.execute_reply": "2026-09-14T02:50:24.100736Z"
+     "iopub.execute_input": "2026-09-14T10:40:52.461552Z",
+     "iopub.status.busy": "2026-09-14T10:40:52.461552Z",
+     "iopub.status.idle": "2026-09-14T10:40:57.497123Z",
+     "shell.execute_reply": "2026-09-14T10:40:57.496611Z"
     },
     "papermill": {
-     "duration": 7.203644,
-     "end_time": "2026-09-14T02:50:24.102763",
+     "duration": 5.041273,
+     "end_time": "2026-09-14T10:40:57.498128",
      "exception": false,
-     "start_time": "2026-09-14T02:50:16.899119",
+     "start_time": "2026-09-14T10:40:52.456855",
      "status": "completed"
     },
     "tags": []
@@ -1345,18 +1268,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "alpha/r=1.0  ||B@A||=1.2087  ||A||=3.9232  ||B||=0.4396\n"
+      "alpha/r=1.0  ||B@A||=1.2088  ||A||=3.9234  ||B||=0.4396\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "alpha/r=2.0  ||B@A||=1.7818  ||A||=3.6063  ||B||=0.3849\n"
+      "alpha/r=2.0  ||B@A||=1.7821  ||A||=3.6065  ||B||=0.3850\n"
      ]
     }
    ],
    "source": [
+    "# Corrige Exercice 2\n",
+    "\n",
     "# Exercice 2 -- on re-entraine brievement r=4 pour les deux scalings et on lit ||B @ A||\n",
     "def train_and_measure(r, scaling, epochs=2):\n",
     "    alpha = scaling * r\n",
@@ -1390,41 +1315,22 @@
     "    )"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "07db3164",
-   "metadata": {
-    "papermill": {
-     "duration": 0.008761,
-     "end_time": "2026-09-14T02:50:24.121948",
-     "exception": false,
-     "start_time": "2026-09-14T02:50:24.113187",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "### Exercice 3 : seuil d'amplification — combien d'epochs pour diverger ?\n",
-    "\n",
-    "Pour `r = 4` et `alpha/r = 2.0`, entraîner **6 epochs** (au lieu de 2). À chaque epoch, mesurer l'exactitude test. Identifier l'epoch où l'exactitude **plafonne ou redescend** — c'est le seuil au-delà duquel le scaling amplifié devient contre-productif."
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "910d1c53",
+   "execution_count": 14,
+   "id": "939fdddf",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-14T02:50:24.140660Z",
-     "iopub.status.busy": "2026-09-14T02:50:24.139645Z",
-     "iopub.status.idle": "2026-09-14T02:50:37.614164Z",
-     "shell.execute_reply": "2026-09-14T02:50:37.613156Z"
+     "iopub.execute_input": "2026-09-14T10:40:57.509753Z",
+     "iopub.status.busy": "2026-09-14T10:40:57.509753Z",
+     "iopub.status.idle": "2026-09-14T10:41:05.742915Z",
+     "shell.execute_reply": "2026-09-14T10:41:05.742387Z"
     },
     "papermill": {
-     "duration": 13.484188,
-     "end_time": "2026-09-14T02:50:37.615164",
+     "duration": 8.240761,
+     "end_time": "2026-09-14T10:41:05.744496",
      "exception": false,
-     "start_time": "2026-09-14T02:50:24.130976",
+     "start_time": "2026-09-14T10:40:57.503735",
      "status": "completed"
     },
     "tags": []
@@ -1477,11 +1383,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "  6  | 0.6155\n"
+      "  6  | 0.6150\n"
      ]
     }
    ],
    "source": [
+    "# Corrige Exercice 3\n",
+    "\n",
     "# Exercice 3\n",
     "torch.manual_seed(SEED)\n",
     "m = copy.deepcopy(base_model)\n",
@@ -1507,33 +1415,6 @@
     "    acc = evaluate(m, test_loader, transform=None)\n",
     "    print(f\"  {epoch}  | {acc:.4f}\")"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "11a7d134",
-   "metadata": {
-    "papermill": {
-     "duration": 0.008669,
-     "end_time": "2026-09-14T02:50:37.634494",
-     "exception": false,
-     "start_time": "2026-09-14T02:50:37.625825",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "## Résumé\n",
-    "\n",
-    "Trois régularités mesurées sur la même mini-tâche, six valeurs de rang, trois scalings :\n",
-    "\n",
-    "- **`alpha/r = 1.0`** est le choix de référence : la magnitude effective du signal est neutre, et le rang commande seul la dimensionnalité.\n",
-    "- **`r = 1` est trop petit** sur Fashion-MNIST inversé : un seul degré de liberté sous-adapte. Au-delà de `r = 4`, les gains saturent.\n",
-    "- **`alpha/r = 2.0`** peut dégrader : trop de signal amplifié, oscillations ou divergence sur les grands modèles.\n",
-    "\n",
-    "Le découplage `alpha / r` est ce qui rend les deux knobs **réglables indépendamment** : augmenter `r` ne change pas la magnitude du signal, et changer `alpha` sans toucher `r` ne change pas la dimensionnalité. C'est l'ingrédient qui rend LoRA **praticable** sur des modèles 7B+ — où le rang reste petit (`r = 8` typique) mais le scaling devient un hyperparamètre de calibration fin.\n",
-    "\n",
-    "**Branchement** : FT-00a (mécanisme) → FT-00b (réglage) → FT-01 (LoRA avec `peft` sur GPT-2) → FT-02 (QLoRA 4-bit) → FT-06 (vision-langage)."
-   ]
   }
  ],
  "metadata": {
@@ -1556,14 +1437,14 @@
   },
   "papermill": {
    "default_parameters": {},
-   "duration": 116.611506,
-   "end_time": "2026-09-14T02:50:38.533872",
+   "duration": 73.051567,
+   "end_time": "2026-09-14T10:41:06.318184",
    "environment_variables": {},
    "exception": null,
-   "input_path": "FT-00b-LoRA-Hyperparams-from-scratch.ipynb",
+   "input_path": "MyIA.AI.Notebooks/GenAI/FineTuning/FT-00b-LoRA-Hyperparams-from-scratch.ipynb",
    "output_path": "FT-00b-LoRA-Hyperparams-from-scratch.ipynb",
    "parameters": {},
-   "start_time": "2026-09-14T02:48:41.922366",
+   "start_time": "2026-09-14T10:39:53.266617",
    "version": "2.6.0"
   }
  },