diff --git a/examples/Reinforcement_Fine_Tuning.ipynb b/examples/Reinforcement_Fine_Tuning.ipynb
index 6bd67eefd2..fcb7193209 100644
--- a/examples/Reinforcement_Fine_Tuning.ipynb
+++ b/examples/Reinforcement_Fine_Tuning.ipynb
@@ -61,9 +61,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/theophile/Documents/repos/jupyter-env/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -97,7 +105,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -130,7 +138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -153,7 +161,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -215,7 +223,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -246,7 +254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -269,7 +277,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -294,7 +302,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -431,7 +439,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -544,49 +552,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Grading predictions: 100%|██████████| 100/100 [00:00<00:00, 329740.88it/s]\n"
+      "Grading predictions: 100%|██████████| 100/100 [00:00<00:00, 610524.60it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'total_samples': 100, 'accuracy': 0.5716752010712578}\n"
+      "{'total_samples': 100, 'accuracy': 0.590985993228499}\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Grading predictions: 100%|██████████| 100/100 [00:00<00:00, 497544.96it/s]\n"
+      "Grading predictions: 100%|██████████| 100/100 [00:00<00:00, 311612.48it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'total_samples': 100, 'accuracy': 0.5855097792577905}\n"
+      "{'total_samples': 100, 'accuracy': 0.5750433490539723}\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Grading predictions: 100%|██████████| 100/100 [00:00<00:00, 414456.92it/s]"
+      "Grading predictions: 100%|██████████| 100/100 [00:00<00:00, 769597.06it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'total_samples': 100, 'accuracy': 0.5702082734545793}\n"
+      "{'total_samples': 100, 'accuracy': 0.5943742483874717}\n"
      ]
     },
     {
@@ -625,7 +633,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -633,32 +641,32 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "Total mistakes: 84\n",
+      "Total mistakes: 86\n",
       "\n",
-      "[Sample 16]\n",
-      "  Model prediction: enveloped double stranded linear dna virus\n",
-      "  Reference answer: double-stranded, enveloped dna virus\n",
-      "  Score: 0.85\n",
+      "[Sample 18]\n",
+      "  Model prediction: acute anterior uveitis\n",
+      "  Reference answer: recurring eye redness and pain\n",
+      "  Score: 0.3596153846153846\n",
       "\n",
       "[Sample 19]\n",
-      "  Model prediction: gallstone ileus\n",
-      "  Reference answer: gall stone ileus\n",
-      "  Score: 0.8225806451612904\n",
+      "  Model prediction: 390 meq\n",
+      "  Reference answer: 150 meq\n",
+      "  Score: 0.6071428571428571\n",
       "\n",
       "[Sample 20]\n",
-      "  Model prediction: acute rheumatic fever\n",
-      "  Reference answer: postinfectious glomerulonephritis\n",
-      "  Score: 0.22037037037037036\n",
+      "  Model prediction: adamts13 deficiency\n",
+      "  Reference answer: decreased adamts13 activity in serum\n",
+      "  Score: 0.5037037037037037\n",
       "\n",
       "[Sample 22]\n",
-      "  Model prediction: amygdala\n",
-      "  Reference answer: hippocampus\n",
-      "  Score: 0.17894736842105263\n",
+      "  Model prediction: todd paralysis\n",
+      "  Reference answer: seizure\n",
+      "  Score: 0.16190476190476194\n",
       "\n",
       "[Sample 23]\n",
-      "  Model prediction: hypopituitarism\n",
-      "  Reference answer: pituitary adenoma\n",
-      "  Score: 0.47812499999999997\n"
+      "  Model prediction: hypokalemia\n",
+      "  Reference answer: hypomagnesemia\n",
+      "  Score: 0.612\n"
      ]
     }
    ],
@@ -694,22 +702,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x132843e90>"
+       "<matplotlib.legend.Legend at 0x125f6b7a0>"
       ]
      },
-     "execution_count": 84,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -734,49 +742,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Grading predictions: 100%|██████████| 100/100 [00:00<00:00, 489988.79it/s]\n"
+      "Grading predictions: 100%|██████████| 100/100 [00:00<00:00, 820803.13it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'total_samples': 100, 'accuracy': 0.6150339441350683}\n"
+      "{'total_samples': 100, 'accuracy': 0.6186850707880021}\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Grading predictions: 100%|██████████| 100/100 [00:00<00:00, 507170.98it/s]\n"
+      "Grading predictions: 100%|██████████| 100/100 [00:00<00:00, 523633.46it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'total_samples': 100, 'accuracy': 0.5901906182115139}\n"
+      "{'total_samples': 100, 'accuracy': 0.6149897683385446}\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Grading predictions: 100%|██████████| 100/100 [00:00<00:00, 543303.63it/s]"
+      "Grading predictions: 100%|██████████| 100/100 [00:00<00:00, 515270.76it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'total_samples': 100, 'accuracy': 0.5927679005876193}\n"
+      "{'total_samples': 100, 'accuracy': 0.6254662232084496}\n"
      ]
     },
     {
@@ -802,7 +810,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -863,12 +871,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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nnXVW8NJLL0V+h5999tlu3+/QoUPdY6dOnZrpY8aPH+8e8/rrr8d+R3rP+jfQ3+FBBx0UnHHGGcHcuXMzPG/s2LHufev9lylTxv0bTJkyJfZ9/Y3ceuut7verv52WLVsGP//8c6btURO9H7Wc7dSpk3uNAw44wL3GggULIq/hf8/dunVzvzMdd5UqVdxjwi1vvTPPPDPSOhhA3pdP/8npYAUAAOReWgn6+uuvXfcnAKmDGgUAAJAp1YAofU4pVgBSCzUKAAAgQnUkqkV58sknXV2C6l0ApBZWFAAAQMSHH37oVhEUMKj4X5sUAkgtORooqA2cOlyo64P6amtDl92ZPn26NWjQwHWMUCcJtfoDAABZS53DVMao3bLVGhZA6snRQGHjxo2uBZta3O0JzWqorZ5azs2fP9/tPKl2cmofBwAAACDr5JquR1pRUI9m9QTPzK233uoKqsK7SWpbevVGV99yAAAAAClYzKyNY5o3b57hPm18o5WFzGzZssXdPG2Eo01ltPmMghMAAAAgVQRBYOvXr3ep/9o1Ps8ECsuXL7eKFStmuE9fr1u3zjZv3mzFihWLPEc7Xg4YMCAbjxIAAADI3RYvXmxVqlTJO4HCvujdu7f16tUr9vXatWutWrVq7pdTqlSpHD02AAAAIDtpgr1q1apWsmTJ3T42qQIFtWZbsWJFhvv0tS74E60miLoj6RZPzyFQAAAAQCrKtwcp+Em1j0KTJk1s6tSpGe6bMmWKux8AAABA1snRQGHDhg2uzaluvv2pPk9PT4+lDaWlpcUef/XVV9vChQvtlltusQULFtijjz5qL7zwgt1www059h4AAACAvChHA4XPP//c6tev726iWgJ93q9fP/f1smXLYkGD1KhRw7VH1SqC9l8YOnSo21penY8AAAAA5MF9FLKzgKN06dKuqJkaBQAAAKSSdXtxLZxUNQoAAAAAsgeBAgAAAIAIAgUAAAAAEQQKAAAAACIIFAAAAABEECgAAAAAiCBQAAAAABBBoAAAAAAggkABAAAAQASBAgAAAIAIAgUAAAAAEQQKAAAAACIIFAAAAABEECgAAAAAiCBQAAAAABBBoAAAAAAggkABAAAAQASBAgAAAIAIAgUAAAAAEQQKAAAAACIIFAAAAABEECgAAAAAiCBQAAAAABBBoAAAAAAggkABAAAAQASBAgAAAIAIAgUAAAAAEQQKAAAAACIIFAAAAABEECgAAAAAiCBQAAAAABBBoAAAAAAggkABAAAAQASBAgAAAIAIAgUAAAAAEQQKAAAAACIIFAAAAABEECgAAAAAiCBQAAAAABBBoAAAAAAggkABAAAAQASBAgAAAIAIAgUAAAAAEQQKAAAAACIIFAAAAABEECgAAAAAiCBQAAAAABBBoAAAAAAggkABAAAAQASBAgAAAIAIAgUAAAAAEQQKAAAAACIIFAAAAABEECgAAAAAiCBQAAAAABBBoAAAAAAggkABAAAAQASBAgAAAIAIAgUAAAAAEQQKAAAAACIIFAAAAABEECgAAAAAyH2BwsiRI6169epWtGhRa9y4sc2ZM2eXjx82bJgdeeSRVqxYMatatardcMMN9vfff2fb8QIAAACpIEcDhUmTJlmvXr2sf//+Nm/ePKtbt661bNnSVq5cmfDxzz33nN12223u8d9//72NGTPGvcbtt9+e7ccOAAAA5GU5Gig8+OCD1rVrV+vUqZMdffTRNmrUKCtevLiNHTs24eNnzpxpJ554ol188cVuFaJFixbWoUOH3a5CAAAAAEiSQGHr1q02d+5ca968+f8OJn9+9/WsWbMSPqdp06buOT4wWLhwob399tt25plnZttxAwAAAKmgYE794NWrV9uOHTusYsWKGe7X1wsWLEj4HK0k6HknnXSSBUFg27dvt6uvvnqXqUdbtmxxN2/dunVZ+C4AAACAvCnHi5n3xvTp0+2ee+6xRx991NU0vPLKK/bWW2/ZoEGDMn3O4MGDrXTp0rGbCqABAAAA7Fq+QFPzOZR6pHqEl156ydq2bRu7v2PHjrZmzRp7/fXXI89p1qyZnXDCCXb//ffH7nv22WftyiuvtA0bNrjUpT1ZUVCwsHbtWitVqtR+eW8AAABAbqRrYU2e78m1cI6tKBQuXNgaNmxoU6dOjd23c+dO93WTJk0SPmfTpk2RYKBAgQLuY2bxTpEiRdwvIXwDAAAAkEtrFEStUbWC0KhRIzv++OPdHgkbN250XZAkLS3NKleu7NKH5Oyzz3adkurXr+/2XPj555+tb9++7n4fMAAAAABI8kChffv2tmrVKuvXr58tX77c6tWrZ5MnT44VOKenp2dYQejTp4/ly5fPfVyyZIkddNBBLki4++67c/BdAAAAAHlPjtUoJENeFgAAAJCXJEWNAgAAAIDci0ABAAAAQASBAgAAAIAIAgUAAAAAEQQKAAAAACIIFAAAAABEECgAAAAAiCBQAAAAABBBoAAAAAAggkABAAAAQASBAgAAAIAIAgUAAAAAEQQKAAAAACIIFAAAAABEECgAAAAAiCBQAAAAABBBoAAAAAAggkABAAAAQASBAgAAAIAIAgUAAAAAEQQKAAAAACIIFAAAAABEECgAAAAAiCBQAAAAABBBoAAAAAAggkABAAAAQASBAgAAAIAIAgUAAAAAEQQKAAAAACIIFAAAAABEECgAAAAAiCBQAAAAABBBoAAAAAAggkABAAAAQASBAgAAAIAIAgUAAAAAEQQKAAAAACIIFAAAAABEECgAAAAAiCBQAAAAKW3ixInWoEEDK1asmJUtW9batWtnv/zyy26f9+uvv9rll19uBx98sBUuXNgqVqxorVu3trVr17rvf/vtt+77tWvXtlKlSlnp0qWtYcOGNmbMmGx4V8A/R6AAAABSli7aO3ToYF988YW74N+xY4e9/PLL1rRpU1u+fHmmz/vxxx/tuOOOs6eeesrWrVtnRx11lAsypkyZYuvXr3eP+eyzz9z3V65caTVr1rRt27bZvHnz7IorrrAhQ4Zk47sE9g2BAgAASElbt2612267zX1+/vnn28KFC+3777+3kiVLuov7e+65J9Pndu/e3f744w875ZRTbMmSJfbll1+652o1oVKlSu4x1apVsxdffNFWrVpl8+fPd9/XqoJMmDAhm94lsO8IFAAAQErSjP/q1atjgYIccsghdsIJJ7jPJ0+enPB5f/31l7333nvu8zJlylijRo1ccKHnffzxx1awYEH3vVNPPdWlMRUoUMB9feihh7rgQYoUKZIN7xD4ZwgUAABASlq8eHHs8woVKsQ+V62BpKenJ3zeTz/9ZEEQuM9feeUV27lzpxUtWtQ+/fRTO+OMM9zHRGbMmOHqFqRr165Z+l6A/YFAAQAAIMQHAZnZvn177PPmzZu7wueff/7Z1SioxuGxxx6LPOftt992hc4KKpS2RKCAZECgAAAAUlLVqlVjn6smIf5znyYUr3LlyrHPlXaUL18+V3twxBFHuPsWLVqU4fEKHNq0aWMbNmywgQMH2sMPP5zl7wXYHwgUAABASlLXonLlyrnP1elIli5darNnz3aft2rVyn1Ue1PdRowYEas1qFWrlvt87ty5bgVCnY/UCUn893T/LbfcYtdee62rU3j22Wetb9++OfBOgX2TL9jd+loeo/+RFfWrK4F6GgMAgNQ1evRou+qqq9znNWrUcJ2MdK1Qvnx518lIxc1aMZD+/fvbnXfeGatNUKGyLqPU+lQtUdXdqESJEq5IWu1Sn3/+ebv44ovd4/V6hx12WIaf7QMSILdeC7OiAAAAUtaVV17pZvrr1avnVhMUFJx33nk2c+ZMFyRkRo957bXX3KqEnpc/f35r27atff755y5IkC1btsQer+5KKnIO34DcjhUFAAAAIEWsY0UBAAAAwD9BoADkgIkTJ1qDBg2sWLFirp2e8lzVXm9XLr/8crckHn+rUqVKhsd9/fXXbuMgdeVQX+9jjz3Wxo0bt5/fEQAAyGv+b+tAANlmzJgxdsUVV2QonFO3jY8++sgVzlWqVGmXz1cAEA4OwpsEfffdd25n0E2bNrkARJ03FDh07tzZLTH27NlzP74zAACQl7CiAGSjrVu32m233eY+16z/woUL7fvvv7eSJUu6vt333HPPbl9DQYY6ZfjbG2+8Efve+PHjXZBQpEgRt3OogoTbb7/dfU+dOjZv3rwf3x0AAMhLCBSAbKSWeep84QMFUVcNrQLI5MmTd/saw4YNc4GANgq66KKLMqQsacdPz7fzUycO0YqCfj4AAMCeIFAAstHixYsTpgxVrFjRfUxPT9/l8wsXLmwHH3ywSz36/fffbdKkSa4135IlS2Lt+rSpj1ryKe1I9Ql333137Pn+cQAAALtDoADkAnvSpfimm25y9QxKVdIqwqhRo9z9f/31V6xYuWnTpvb6669b48aNXbCgx6elpcVeo1ChQvvxXQAAgLyEQAHIRkoX8lSTEP95tWrVMn3uMcccYwcccEDs60suuST2eXglonXr1q52QbuEagWhZcuWse8deeSRWfROACA1PPjgg24Vd29veh6Q7AgUgGykNKFy5cq5z9XpSLSjpy7spVWrVu5j7dq13W3EiBGx5/bv399WrVqVocWqV7169djnH374YYZUJxUxy7/+9S8XbAAA9m5zKk267O1NzwOSHe1RgWykGgN1NrrqqqtcoFCzZk2XHqTZ//Lly8c6Iv3www/uoy98loEDB9pdd93lnqNUJV/ErHaqvt2qX1EoXry4q3tQ5yOlIOnrJ554IlbgDADYM9q5Vm2p4y1btsw1kFDDCNWOJXoekOwIFIBsduWVV1qJEiXsgQcecPUG2hRNRcj33nuv64CUGRUlv/POO/bjjz+6marDDz/cmjdvbn369MlQGH322We7VQUFG2q7qsBBqxEqbAaQWPrAOjl9CMil2ul2ZZnI/Y2HrrDl63ZahQPy28wE37cN4yx9IJtdIrFq/b62ZJAv2JMqyjxEF1ilS5d2rSKJ9gEAQqCAzDwxc7U9Oet/q7veyvXbbWdglj+fWYWS0XnXK5qUt65Ny2fTUSLZVMvBQGFvroVZUQAAAMjEhi07bPm67Zl+X8FCou/reUCyy/Fi5pEjR7pCTKVfqKXjnDlzdvn4NWvW2HXXXefyAbXp1BFHHGFvv/12th0vAABIHQcUKWCVShXc65ueByS7HF1R0GZRvXr1cv3gFSRox1m1clRudTjn2tu6daudfvrp7nsvvfSSKy767bff7MADD8yR4wcAAHmb0odIIUKqytFAQT2Gu3btap06dXJfK2B46623bOzYsbHuL2G6/88//7SZM2fGNo4Kt4UEAAAAkOSpR1odmDt3ruvaEjuY/Pnd17NmzUr4nDfeeMOaNGniUo/U+lE94dVqcseOzPMA1RpSRRvhGwAAAIBcGiioP7wu8HXBH6avly9fnvA5CxcudClHep7qEvr27WtDhw51veUzM3jwYFfZ7W/hnXEBAAAA5IGuR9rYRPUJo0ePtgIFCljDhg3d7of333+/6xOfSO/evV0dhKcVBYIF5GZKydNtb+nvPPy3DgAAkJSBgnah1cX+ihUrMtyvr7XTbCLqdKTaBD3PO+qoo9wKhFKZtOttPHVG0g1IFgpmFQDvy/MAAACSPlDQRb1WBKZOnWpt27aNrRjo627duiV8zoknnmjPPfdcbMt00S61CiASBQlAMtLmJ+roFW/ZsmWxv339zSd6HgAAQJ5IPVKaRMeOHa1Ro0Z2/PHHu/aoGzdujHVBSktLcxdMqjOQa665xkaMGGE9evSw66+/3n766SdXzNy9e/ecfBtAlsoshahKlSpupUFBwu+//54jxwYAAFJHjgYK7du3t1WrVlm/fv1c+lC9evVs8uTJsQLn9PT02MqBqLbg3XfftRtuuMGOPfZYF0QoaLj11lstmTS8+emcPgQkoZVrN8U+8jeEvTX3/rScPgQAQJLJ8WJmpRlllmo0ffr0yH1qjzp79uxsODIAAAAgdeV4oAAgoxWfT7aVn0+O3L9t45rYx69H9Yx8v0KjVlaxUatsOUYAAJD37XWgoJ2QO3fubJdffrlVq1Zt/xwVkMJ2bNls2zb8lfkDgiDh9/U8AACAHNtwrWfPnvbKK69YzZo17fTTT7eJEye63Y8BZI0CRYpZoQPK7PVNzwMAAMgq+YIgCPblifPmzbPx48fb888/73ZKvvjii91KQ4MGDSw3U6957dC8du3aHGsnSSEqgOxGMfOupQ+sk9OHACCFVOv3dVJcC+/1ioKngOCRRx6xpUuXul2Rn3zySTvuuONc56KxY8faPsYfAAAAAJK5mHnbtm326quv2rhx42zKlCl2wgknWJcuXVx/99tvv93ef/99tzkaAAAAgBQIFJRypOBAKUfa40Cboj300ENWu3bt2GPOPfdct7oAAAAAIEUCBQUAKmJ+7LHHrG3btlaoUKHIY2rUqGEXXXRRVh0jAAAAgNweKCxcuNAOPfTQXT6mRIkSbtUBAAAAQHLa62LmlStX2qeffhq5X/d9/vnnWXVcAAAAAJIpULjuuuts8eLFkfuXLFnivgcAAAAgBQOF7777LuFeCfXr13ffAwAAAJCCgUKRIkVsxYoVkfuXLVtmBQvuc7dVAAAAAMkcKLRo0cJ69+7tdnPz1qxZ4/ZOUDckAAAAAMlvr5cAHnjgAfv3v//tOh8p3Ujmz59vFStWtGeeeWZ/HCMAAACA3B4oVK5c2b766iubMGGCffnll1asWDHr1KmTdejQIeGeCgAAAACSzz4VFWifhCuvvDLrjwYAAABArrDP1cfqcJSenm5bt27NcH+bNm2y4rgAAAAAJNvOzOeee659/fXXli9fPguCwN2vz2XHjh1Zf5QAAAAAcnfXox49eliNGjXcDs3Fixe3b7/91mbMmGGNGjWy6dOn75+jBAAAAJC7VxRmzZpl06ZNs/Lly1v+/Pnd7aSTTrLBgwdb9+7d7Ysvvtg/RwoAAAAg964oKLWoZMmS7nMFC0uXLnWfq13qDz/8kPVHCAAAACD3rygcc8wxri2q0o8aN25sQ4YMscKFC9vo0aOtZs2a++coAQAAAOTuQKFPnz62ceNG9/nAgQPtrLPOsmbNmlm5cuVs0qRJ++MYAQAAAOT2QKFly5axzw8//HBbsGCB/fnnn1amTJlY5yMAAAAAKVSjsG3bNitYsKB98803Ge4vW7YsQQIAAACQqoFCoUKFrFq1auyVAAAAAORxe9316I477rDbb7/dpRsBAAAAyJv2ukZhxIgR9vPPP9shhxziWqKWKFEiw/fnzZuXlccHAAAAIBkChbZt2+6fIwEAAACQvIFC//7998+RAAAAAEjeGgUAAAAAed9eryjkz59/l61Q6YgEAAAApGCg8Oqrr0b2Vvjiiy/sqaeesgEDBmTlsQEAAABIlkDhnHPOidzXrl07+9e//mWTJk2yLl26ZNWxAQAAAMghWVajcMIJJ9jUqVOz6uUAAAAAJHugsHnzZnvkkUescuXKWfFyAAAAAJIt9ahMmTIZipmDILD169db8eLF7dlnn83q4wMAAACQDIHCQw89lCFQUBekgw46yBo3buyCCAAAAAApGChcfvnl++dIAAAAACRvjcK4cePsxRdfjNyv+9QiFQAAAEAKBgqDBw+28uXLR+6vUKGC3XPPPVl1XAAAAACSKVBIT0+3GjVqRO4/9NBD3fcAAAAApGCgoJWDr776KnL/l19+aeXKlcuq4wIAAACQTIFChw4drHv37vbBBx/Yjh073G3atGnWo0cPu+iii/bPUQIAAADI3V2PBg0aZIsWLbLTTjvNChb8v6fv3LnT0tLSqFEAAAAAUjVQKFy4sE2aNMnuuusumz9/vhUrVszq1KnjahQAAAAApGig4NWqVcvdAAAAAOQ9e12jcP7559t9990XuX/IkCF2wQUXZNVxAQAAAEimQGHGjBl25plnRu4/44wz3PcAAAAApGCgsGHDBlenEK9QoUK2bt26rDouAAAAAMkUKKhwWcXM8SZOnGhHH310Vh0XAAAAgGQqZu7bt6+dd9559ssvv9ipp57q7ps6dao999xz9tJLL+2PYwQAAACQ2wOFs88+21577TW3Z4ICA7VHrVu3rtt0rWzZsvvnKAEAAADk/vaorVu3djdRXcLzzz9vN910k82dO9ft1AwAAAAgxWoUPHU46tixox1yyCE2dOhQl4Y0e/bsrD06AAAAALl/RWH58uU2fvx4GzNmjFtJuPDCC23Lli0uFYlCZgAAACAFVxRUm3DkkUfaV199ZcOGDbOlS5fa8OHD9+/RAQAAAMjdKwrvvPOOde/e3a655hqrVavW/j0qAAAAAMmxovDxxx/b+vXrrWHDhta4cWMbMWKErV69ev8eHQAAAIDcHSiccMIJ9sQTT9iyZcvsqquuchusqZB5586dNmXKFBdEAAAAAEjRrkclSpSwzp07uxWGr7/+2m688Ua79957rUKFCtamTZv9c5QAAAAAkqM9qqi4eciQIfb777+7vRT21ciRI6169epWtGhRl9Y0Z86cPXqeVjXy5ctnbdu23eefDQAAACCLAwWvQIEC7mL9jTfe2OvnTpo0yXr16mX9+/e3efPmuV2eW7ZsaStXrtzl8xYtWuQ2eWvWrNk/OHIAAAAA+y1Q+CcefPBB69q1q3Xq1MntxTBq1CgrXry4jR07NtPnaPfnSy65xAYMGGA1a9bM1uMFAAAAUkGOBgpbt261uXPnWvPmzf93QPnzu69nzZqV6fMGDhzoaiK6dOmSTUcKAAAApJa92pk5q6m9qlYHKlasmOF+fb1gwYKEz1ERtXaGnj9//h79DO0crZunHaUBAAAA5PLUo72hFqyXXXaZa9Navnz5PXrO4MGDrXTp0rFb1apV9/txAgAAAMkuR1cUdLGvQugVK1ZkuF9fV6pUKfL4X375xRUxn3322bH7tI+DFCxY0H744Qc77LDDMjynd+/erlg6vKJAsAAAAADk4kChcOHCbqfnqVOnxlqc6sJfX3fr1i3y+Nq1a7u9G8L69OnjVhoefvjhhAFAkSJF3A0AAABAkgQKotn+jh07WqNGjez444+3YcOG2caNG10XJElLS7PKlSu7FCLts3DMMcdkeP6BBx7oPsbfDwAAACCJA4X27dvbqlWrrF+/frZ8+XKrV6+eTZ48OVbgnJ6e7johAQAAAEihQEGUZpQo1UimT5++y+eOHz9+Px0VAAAAkLqYqgcAAAAQQaAAAAAAIIJAAQAAAEAEgQIAAACACAIFAAAAABEECgAAAAAiCBQAAAAARBAoAAAAAIggUAAAAAAQQaAAAAAAIIJAAQAAAEAEgQIAAACACAIFAAAAABEECgAAAAAiCBQAAAAARBAoAAAAAIggUAAAAAAQQaAAAAAAIIJAAQAAAEAEgQIAAACACAIFAAAAABEECgAAAAAiCBQAAAAARBAoAAAAAIggUAAAAAAQQaAAAAAAIIJAAQAAAEAEgQIAAACACAIFAAAAABEECgAAAAAiCBQAAAAARBAoAAAAAIggUAAAAAAQQaAAAAAAIIJAAQAAAEAEgQIAAACACAIFAAAAABEECgAAAAAiCBQAAAAARBAoAAAAAIggUAAAAAAQQaAAAAAAIIJAAQAAAEAEgQIAAACACAIFAAAAABEECgAAAAAiCBQAAAAARBAoAAAAAIggUAAAAAAQQaAAAAAAIIJAAQAAAEAEgQIAAACACAIFAAAAABEECgAAAAAiCBQAAAAARBAoAAAAAIggUAAAAAAQQaAAAAAAIIJAAQAAAEAEgQIAAACACAIFAAAAABEECgAAAAAiCBQAAAAA5M5AYeTIkVa9enUrWrSoNW7c2ObMmZPpY5944glr1qyZlSlTxt2aN2++y8cDAAAASMJAYdKkSdarVy/r37+/zZs3z+rWrWstW7a0lStXJnz89OnTrUOHDvbBBx/YrFmzrGrVqtaiRQtbsmRJth87AAAAkFfleKDw4IMPWteuXa1Tp0529NFH26hRo6x48eI2duzYhI+fMGGCXXvttVavXj2rXbu2Pfnkk7Zz506bOnVqth87AAAAkFflaKCwdetWmzt3rksfih1Q/vzua60W7IlNmzbZtm3brGzZsgm/v2XLFlu3bl2GGwAAAIBcHCisXr3aduzYYRUrVsxwv75evnz5Hr3GrbfeaoccckiGYCNs8ODBVrp06dhNqUoAAAAAcnnq0T9x77332sSJE+3VV191hdCJ9O7d29auXRu7LV68ONuPEwAAAEg2BXPyh5cvX94KFChgK1asyHC/vq5UqdIun/vAAw+4QOH999+3Y489NtPHFSlSxN0AAAAAJMmKQuHCha1hw4YZCpF9YXKTJk0yfd6QIUNs0KBBNnnyZGvUqFE2HS0AAACQOnJ0RUHUGrVjx47ugv/444+3YcOG2caNG10XJElLS7PKlSu7WgO57777rF+/fvbcc8+5vRd8LcMBBxzgbgAAAADyQKDQvn17W7Vqlbv410W/2p5qpcAXOKenp7tOSN5jjz3muiW1a9cuw+toH4Y777wz248fAAAAyItyPFCQbt26uVtmG6yFLVq0KJuOCgAAAEhdSd31CAAAAMD+QaAAAAAAIIJAAQAAAEAEgQIAAACACAIFAAAAABEECgAAAAAiCBQAAAAARBAoAAAAAIggUAAAAAAQQaAAAAAAIIJAAQAAAEAEgQIAAACACAIFAAAAABEECgAAAAAiCBQAAAAARBAoAAAAAIggUAAAAAAQQaAAAAAAIIJAAQAAAEAEgQIAAACACAIFAAAAABEECgAAAAAiCBQAAAAARBAoAAAAAIggUAAAAAAQQaAAAAAAIIJAAQAAAEAEgQIAAACACAIFAAAAABEECgAAAAAiCBQAAAAARBAoAAAAAIggUAAAAAAQQaAAAAAAIIJAAQAAAEAEgQIAAACACAIFAAAAABEECgAAAAAiCBQAAAAARBAoAAAAAIggUAAAAAAQQaAAAAAAIIJAAQAAAEAEgQIAAACACAIFAAAAABEECgAAAAAiCBQAAAAARBAoAAAAAIggUAAAAAAQQaAAAAAAIIJAAQAAAEAEgQIAAACACAIFAAAAABEECgAAAAAiCBQAAAAARBAoAAAAAIggUAAAAAAQQaAAAAAAIIJAAQAAAEAEgQIAAACACAIFAAAAABEECgAAAAByZ6AwcuRIq169uhUtWtQaN25sc+bM2eXjX3zxRatdu7Z7fJ06deztt9/OtmMFAAAAUkGOBwqTJk2yXr16Wf/+/W3evHlWt25da9mypa1cuTLh42fOnGkdOnSwLl262BdffGFt27Z1t2+++Sbbjx0AAADIq3I8UHjwwQeta9eu1qlTJzv66KNt1KhRVrx4cRs7dmzCxz/88MPWqlUru/nmm+2oo46yQYMGWYMGDWzEiBHZfuwAAABAXpWjgcLWrVtt7ty51rx58/8dUP787utZs2YlfI7uDz9etAKR2eMBAAAA7L2CloNWr15tO3bssIoVK2a4X18vWLAg4XOWL1+e8PG6P5EtW7a4m7d27Vr3cd26dZZTdmzZnGM/G0BqyskxLxms/3tHTh8CgBSSk2Oy/9lBEOTuQCE7DB482AYMGBC5v2rVqjlyPACQE0oPvzqnDwEA4A0ubTlt/fr1Vrp06dwbKJQvX94KFChgK1asyHC/vq5UqVLC5+j+vXl87969XbG0t3PnTvvzzz+tXLlyli9fvix5H0B2zQAowF28eLGVKlUqpw8HAFIaYzKSlVYSFCQccsghu31sjgYKhQsXtoYNG9rUqVNd5yJ/Ia+vu3XrlvA5TZo0cd/v2bNn7L4pU6a4+xMpUqSIu4UdeOCBWfo+gOykExInJQDIHRiTkYx2t5KQa1KPNNvfsWNHa9SokR1//PE2bNgw27hxo+uCJGlpaVa5cmWXQiQ9evSwk08+2YYOHWqtW7e2iRMn2ueff26jR4/O4XcCAAAA5B05Hii0b9/eVq1aZf369XMFyfXq1bPJkyfHCpbT09NdJySvadOm9txzz1mfPn3s9ttvt1q1atlrr71mxxxzTA6+CwAAACBvyRfsSckzgByn7l1aWVPdTXw6HQAgezEmIxUQKAAAAADIfTszAwAAAMh9CBQAAAAARBAoAAAAAIggUAAAAAAQQaAA5CLaTPCLL76wHTt25PShAEDK++2332zatGluF1sgFREoALmAbz520UUX2dNPP20FChSwbdu2ub1Fwt8HAOx/fsxV+9Nbbrkldv+SJUvcx507d+bYsQHZiUAByIETUPyKwd9//+0+nnbaafbiiy/aYYcd5vpyP/744+7+fPny5cixAkAq0JgcvvjfunWr+/jvf//bfv/9dzv55JPd5q9t2rRx94c3ggXyMv7SgWymi36tGMjKlStt48aNVqxYMZszZ469++67tmLFCrv00kvdyal///45fbgAkOdpTNbFvyZt/vjjDzdRo3SjRx55xP766y876KCDbNasWfbZZ5/l9KEC2YpAAdgPKwbbt29PuDStE4+WrrWcXbZsWTvuuOPsqquusqVLl9rxxx/vTkg6WdWuXdsOOeSQHDl+AMiLKwaZ1X4pxXPy5MlWt25dO/jgg10K6IcffmglS5a02bNnW7NmzaxGjRr2r3/9y43PpIIilRAoAFmUSuRPHloxKFiwYGRpWsHDkUceadddd5199dVX9swzz9gTTzxhM2bMsO7du9vPP//sHnfssce6k9bmzZtz5P0AQLLTRE14skYrBn4lN+yyyy6z9u3b26OPPmqdOnWy9957z43lPXr0sNdff909pkmTJjZv3jz79ddfs/U9ALkBgQLwD+sMfCqRryPYtGmT3X///XbCCSe4E89PP/3kTlgKHpTv+sYbb9gpp5xirVu3thYtWthTTz1lv/zyi6tNEOXAfvzxx2752/8sAMCuaTLG00RNeLJGaZ1nnXWWG3fVXc7XIDRu3NilfSr9s2fPnm6VV5M4NWvWtOHDh7vHtGzZ0pYtW+bGaSDVECgAeyD+Yj1cZ6Bl62HDhtkdd9zhPn///fft888/d7NUmoW69tpr7bvvvnOPbdq0qZUrV84tY3t16tSxRo0auZOXnHvuuZaenu5u/mcBAHZNkzGiSRytBmhMVuqQWpw+++yzbrW2ePHiLt3z1VdfdY/VhE6FChWsWrVqsdepWLGinXPOOTZz5kz39UknneQCiR9//NEFI4zJSCX/938VgAitAihACK8WeOvWrXN1Brrg//rrr12Rm4rgXnvtNVdbMGjQIHcCqlevnjtZvfnmm3bMMce4FYXChQu7ImavfPnyVqlSJfvmm29cS1Q9TieuMWPGuII67atw6qmnuhkuAEhlCgISpRDJc88951Zwq1SpYkOGDLFSpUrZSy+95AqRtVKrNqdaGejVq5cLHDSZo4ma6tWru6YSGsOLFi3qViJ0n4IDpYkqwNBKg8Z3fdTj9L3//Oc/7hxB4IC8jBUFIBQYxC9d+xOSlqZ9DYGsXbvWrRpotUAX+vr8+eefdyeZVatWuSBB9PHQQw91F/uiwEEnL+XBhvNnP/roIxcg+J//wAMP2KJFi1xLPqUx+f0UACDVGkOE+TFZ4+P8+fMzBBAaowcMGGBvv/22G5OnT5/uxtBPP/3U1YGJipU1Luuxeg1Nxqh5hB6jTnOeuhtVrlw59vNuuOEGq1Wrll1wwQVutUErFUKQgLyOQAEpz6cVKTDwS9eyZs0au/rqq91StU4Mmn3q06eP+55mqDTLr5OMit5EM1NKG1LuqwIJ0azTEUcc4Waxvv/++1j6kXJgNbullCTVKOgE1bx5c/d4UdcNBR4bNmywH374wT0HAFKBn0TxjSHE14Q9+eSTVrVqVTfLn5aWZhdffLELJnRBf/rpp7sxWeOwOhaVKFHCLrnkEjeuKnDwjjrqKLeyq0YSoq5GGosVTGjSRimjSk1SEbM6HYnSQx9++GGXfqQx/rbbbsuB3wyQ/QgUkFInn0Tt8XQy0oW9Zu11otEJQScCLVmr3kBFcF9++aXb2+Dee+91y9taOdDqgFKFwmlEOgGJZqc8ndD0s3XyEQUEOoFp5aFz585upkonMxXZhY9J6Ue7a+sHAMneFCK+lbQmbXTfli1b3OSMJls0puoiXSusuqBXB6J77rnHdYhTKpEmdo4++mjXdto3ghDVHqjbnNI/PW1oqfs/+eQT97XSiZRqpJ/30EMPuRTRMmXKuOLmsAMPPNC9fqKVDiCvIlBAyrTIC6cShb+vGSotR48cOdLq16/vag80izVixAhr27atK2TTCoIu6PW1AghtiqagQGlFWub2dEJTIZx6cMcHCtOmTXNfa5ZKJxutSLzzzjv2559/uuXyAw44IOF7yKytHwAkI3+R7ZtCxLeS1iqqVgEUCGgiRquvDRo0cHVbWino0KGDawqhLkaqB1MQoVSg0qVLu7Qijauexm7tUePHX1ENg+rL/H0KJLRKofRP1TZoJfe///1vbOInXnilA8jrCBSQp4Vb5GnpWRf7msX3y9D6ngqJNVM0ZcoUu/HGG92qgm9zqu/ppKCZJjnzzDNdNyJ10VBwoZNX+KSkWSrlsSo3Njx7pUI6PVcBg2auFEyoSE4rC6KVCdqgAkgF/iJ79erVbudj7WWgLkXakFIUDGhs1UV7u3bt7IorrnCruLqAVyqoLvT9KqvShnSfmkqIWk6ruYSnCRgFD2oW4V9f6UmaANIqsVYiRJNESg/1E0taVU60aSaQaggUkOv9kwtozdZr1umFF16wgQMHuuVqXw+glQTRcrUKjHUy0QW8DxK0EuBzWP2MvlYD1FVDJ61ChQq5k43SkjzNaGlVQSsG6ozkg5HevXvb+eefHwta9Npqh+o38NFrURQHIBX069fPNWy46667bOLEie4C/sorr3SpPro4V+c4jZHq9OYnbvzFvFYbwp2P9Lhw+qfGaF3kh2sSNFmjCZoFCxbE7rvwwgutf//+bpLIpx+pwNkXKWtMjl/pAFIR/xcgV9PAHn8BvTc5+zrpLF682BUHq/uF2tspVUi7I993332ubkDpQ6pL8CcIBSaa6VfNgPJfVbugk4ZopUDf1zK1TiKHH364O0n5+gPRyU75sAo+4o/bL7l37NjRLr/8crcqAQDJYsmSJbvtGLc7qiFQOtHSpUvtgw8+cOOyJm5UD/bYY4+5x2gSZuHChbHVXNEEj2q7tPrgJ5A0caOb0odEY6pWJMIrvdpUTasFCgbCE0/hujX9PI3LeowwcQP8fwGQS913331BxYoVgz/++CPh97ds2RIsXLgw2LFjh/t6586d7vNt27YF27dvd/f9/fffQe/evYPy5csH6enpsefq8zp16gQPPfSQ+3rUqFFBmTJl3HO9rVu3BvXr13ePGzNmTDB69Oigdu3awdChQ93PknXr1gVLly6N/XzPHwsA5BWffvppUKhQoeCdd95xXyca4xYvXuzGRT8O6qbxODy2fvDBB0G+fPmCp59+OsNzL7roouDCCy8MNm/eHPz4449B4cKFgw8//NB9zz+/S5cuwaGHHhr07ds3mDx5ctCyZcvg/PPPD1avXh37mT///HPC4/fnBQB7jhUF5Dp+xke5/koF8h2E/MyPZoq0AqD9C9Qa7/rrr3ezXJoB8i1OtSyt9CHlojZs2NDloaodnl7brwgoTUh9tDWrpOVrPc+nGmnpWqsIalF69tln29ChQ91NubSdOnWKzTZp5sqvCoRnoPyxAEBeGZM1HmvG3e8i73P4NY6qc5tSLlu1amVdunRxdQIaB33Bsq9L0GsphUjpm/75/qNWBdQoQumhKjZWa1IVFYePQSvBt956q6sp06qsipqVyqSPop+nVKNEaAoB7D2uZJDjfKs5f7LwJwTl+isY8J0pNMj/8ssvrsuFioOVg6pWecor9T2ttbFZt27dXLcKbaKjAEFBhQIGpQ3pJOJfX4GDgglf0KwUJLU+FX+RrxOXfp7qEJQGdfvtt7u2eQCQlyVK8VQHIY2n2jDSj8lqxHD33Xe7cVW1YK+88oqbQFELU+0fo12Mtf/Aaaed5sZVjcOapKlbt64b2/V8P9761qYKSHTfKaecYpMmTYr9LFFAcM0117g0Jb3+hAkT3EQPgP2DQAG5ZlMdnRg0k+9PGuogpGBBO2SKTkRvvPGGWw3QDJJvRaqTi04WmoVSvqvPf9UJSYVqOrGoPd6wYcNc32y9vlYplNeqdnj+BKiZMAUmEm5951cpfB4u3YkA5EXhsS3cltmvlqq7kMZSrSD89ddf7n4VF6u+S22lVUOgFVat4s6dO9cFFJrQ0bis7kRqG+03j9RKrWoNfCCgjkaqCVMhswINjbtqIe33Nwiv0Oo4fQ1YeJIJQNbLp/yj/fC6QAaamUqUjqM/v82bN1vfvn1dAbCCA7Uw9ZuPqXXeo48+6tKNtBStgmT119bt448/ds/XDJdOUGqhp5mqRMXPDz74oFuu1o6d+r5OXCeeeKI7SfndkAEgVejiWuNnonQcjZkaG7WXjMZnzeCrIYQuzufMmePGUaVi6j7tdaAJHKUJzZw5003YqFuRAoJrr73WTcYkKgxWAwiN3fq+Os5pPNcKr3ZEVmoSgNyBFQXsF/EzPIk21dGMv2bwNROl2SkFC1ohUN3BU089FWtdqhQhv4GZuldolkr1Cy+++KJbFdAs1E033eRWDxKdkPSzNcMlSlHSrpsvv/yyO7nFBwnsgAwgmWU29xd/f6INKNVOWhfu2ulYdQia9ddKq9o7a4wV5f9rXPY7HWu/AwUP6v6mx2k/A6VqqntRnTp1Mu0epPRQjf9qi6qA4eabb3bBQ3yQEN40E0AO2IvCZyBD14r33nsvQ4cf3+Ei3p9//hmMGDEi6NixY/Duu+8GmzZtcvfPnj07qFu3blCuXLngk08+iT3++uuvd52GVqxYEfz+++/BGWecEXTu3Nl97+WXXw6KFy8ezJ8/P8PP0NePP/54wp8veh11PorvspHZ4wEgmSxatCiYNGlS7Gs/HsePcf4+jcXXXntt8MQTTwQrV65031uzZk3Qvn1715FoyJAhsee8/vrrQaVKlYI333zTfd2nT5+gevXq7nN1OCpRooQb48M0fg8fPjxYvnx57OfGa926dXDBBRdk6e8BQNZiRQF7TbPuw4cPd52GNEvlU4p8hwvlmmr52K8CjB492s3+a8apa9eubslaVDysm1YHtEzt+3DrMeqvrcJkFRlr9srvunneeee5HFltlqO+26pf0MY9mo1StwwtXSeimSttxKNiO1EBndArG0BeMHbsWDfmrl271n3tx2M/xqkuSx2ENDuvNB9tNqZ9CjR+qnuQqDZA+wlopVVpRV7Lli1drYBSi0RjsHZVViMJPeeqq65yjSWU3jl//ny3YqsUUtWE+Y0nE4212gxTnea0ouxToQDkLv+r2AR2QwO5X67WiSBMJxwVr6kYWSee3377zRUd64SjdCFtcqbnaSdO1Rzccccd7uJdJxwfOPjUJC1XK2jQhb+eo0BB6UVazlanjPHjx7vcWXUoUtGyahfUtlQpS9ooLRF1PVJNwrPPPuu+9huoAUCy8ik5GjsHDBgQu18X3Oo2pMBBY7ImSLSpmQqN9TjVE6j+S61O33//fRcI6KJeY6TGZD1XTSE0hmss1vipSRuN63o9NZJQ0bHSQLXppF6zSpUqLl1J9WUKCpS2pEkfTQRlRjVnSnfSJBLd5IBcKotXKJDHaIOaREvGWqKeMGFC8PHHH7uvJ06c6JarmzRp4pa0N2zYEDzyyCPuvkcffTT2vF9//TXInz9/MGPGDPe1Nu4pUKBAMGfOnNhjtMFahQoVgrFjx7qvp02bFtSsWTMYPHhwhmNYtmzZXr0XbdAGAMlMaZ7xG4f5dCKNqz79SKlI1apVC4444ohg0KBBLkVI47XG3zZt2mR4vh6njSn12hp/lVbUr1+/DI/Rc84991z3udKJWrVqFTRu3DjDY/Rcv/EZgLyB1CPskmb0NTukVCBtPqYe2aKvVRisTW/kzDPPdDNKap+nDkSa2ddGaOpCtH79+lhakZavVQznn6fPNSPVo0cP19lItMKgomatRIhWFJRapNcN0wxXuN/37patVRQNAMnMr+pqNUHd29T4QWO0xlil/4wcOdLN+iutUysEmrFv166dSxHS123bto2N4V6LFi1ccwmlCand9H/+8x+38quCZI2tWjn45ptv7IwzznCPV8tpdZnTLUzP1ffi98YBkLwIFLBLymVVVwstH6vFqL+YV8eKevXquToFnVx0EtJFv/Yj8DmyohOTb3vnaeMdneBE6UfaVEfdLvTaWhp//PHHXTs+BQiioOHqq692nTES8f2+qTcAkNetWrXK0tLSXKtS7YCsFExtRKl0So23ukjXRb0oxahy5coZxmTtTfDrr7+6sds7//zzXR2YDx5OP/10l3qk3ZVVP6YNLvVRKZ6ifWX0nPhAwQvvjQMgufF/cYrRSWRPW4BqRkg1BSpuW7x4sSsc1iqCNr8RtdHTCUcFbaKTlHJYwzNV6ret1nkqfPO0+qB8WJ2otOKgmoQDDjjA7XqsfFk9VoFCPGanAORFGpP3dHwbM2aM2/BM9QCqDdPEimbxRTsUq1GDNjkT1RtoEkW7yns+CFBr6fB9GzdujG1uqVowXeh37tzZvb5qCJ555hkrWrRo5LgB5G0ECnlc/G7CmunxvbN1skgURPg0IXWi+Oqrr9zJR0vKCgC0aqDCNtEmOXrM999/775WQZw229HslqeZKM2AhU9Uep5ec82aNW7GyW/Io+dpBkzHnOgExOwUgGTnx9lwYOD3mVERsdKGEo3hfkzUxItSPJWaqTFcKwbaQ0Y0fupzdR4SFStrvP7uu+9ir6dC5AoVKrixXZup+Z+vFQP9fFEaqVaNNXGjlWOtXiTqSpRoszYAeQtXXnmUP6no5KOZIZ+Wo4FeHSqUIqQVAM0WqZ1pOIjQ40UnGC1xd+/e3Z2AbrzxRjvnnHNcpyHRiUqP/+GHH9zXej2dwBQU+J+vn6N6hW+//TZ2YtR9OpHpNUW5tOqgoRkr0eM4AQHIS/yY6MfZ8MSH0i6VaqkdjdURTiuznh/D9RyN30r/mT17tnusNivTGK32puoSp53tVQemlQZN4ihoUNqoVn7DK72qC1N6km9dKkoRVZtTUSpps2bNYl3idOy+BTaA1EJ71DxCA3n44tp/rov4e+65x6UHXXfddS7FRyk/r7/+uttZc8iQIe4EoDZ2mvnXiUE7FmumSt9Xr23ltGpZOj093a0C6PV0UtPJSSsMOiktX77cFRdr5UH1B506dXIBgej7qkWIpyVy5dVq2VwpSr7Y2AcqAJDXxmRdnKtIWMGB6rPURlSfK/VSBg4c6FJ9VKSs19D31NpUrU41yaM20Bpn9RjfWlR7yuj5TzzxhKsp02qB2kmrKPnII4+0cePGuQkcTciIfr7G3fgLf61c+NoC7cisFCc/TgNITVyRJQEVq6ku4JJLLnEX04lmd8InJM3Ia/ZfqTzaZEwDv1J9tBmOAgXtc6BlZi0tKzjQnggKFLQZz8SJE12+qrpgaPlZr6vPw3SS8TNemtXS5mrKd1WgoIJn1R34IEESBQniTz46pjvvvDPLf28AsD9ohVRpOZrg0Cy+xmS/8WT8mOz3n3nrrbesX79+rgBYRcL169e3SZMmuYt4FQZfeuml7vFKB+rbt68rQNb4rLFRF/t6rvYo8PsPhGk8VnqRfo5WC/Q81YEpUOjQoYM7D6jWbHdjcniSRpuh6QYgtREo5NKTkJaWNaOj/FN1BPIdgCRRWo5m/1VorBZ3ukjXzNHDDz/sZo10ItJMke7XaoA/2WiDGxWtaclZxcpaBdAtTLNf2kjNX9QraNFJy7+GHq8ZMRXNaVlc9QcAkJfool2bRmpzR41xGhOVyuMvrBONyUoFUjtRrRDoglubl2m3ebUv1Xit19JHBQnh1s8KIPR6akl6wQUXuAt+XxfmAw+lDS1btsylbarVtM4ZviNRw4YN3c/QR9HP0Q0A9gU1CrmIz+HXzL5SgnwBmlYA1H3Cp+Zo9qhnz54Z8kuVAqSiYuWiimaPdLLxs1Q6oaktqXJPP/roo9jzateu7TpZaFlb4ouIVVinThj6+TqhKf1Is13+JKTZK504wyscu9vPAACSicbWQYMGubRMUV3AfffdF0vl0bip72uCx1NbZ6XuqO2oxlE1bdAKrFYD/IW76gC0sjtjxozY87SSq8cqiEgUhOg8oXG4T58+9u9//9tGjx5tF154YSxQUOGxVoWVnuQxJgPYVwQKuYQGf+WCimahdEHv29epE4XalCpPVZSG9Mgjj7gTkKcVB3Wy8EGALuCVCuT7aYtmn9Txwp/s/ONU/BYOHsK0RK0TkJbZNUOmIEazX+G0p/iTEAVvAJKdxjU/JmvlVemb4SJjXchr1VbNIHQxr/oBpWGGu8xprJw5c6YrLNZkivaC0bjuJ2Q0zis9yO9P41d6lRr66aefJqzZ0tcKNsaPH+9WNXQeuOGGG9xrZYYxGcC+IlDIJXQS8cvLWqLWbJBmsRQUaMZfs0bvvvuuOynpYl8X+H4VwJ+UNLukC3qd3JRypC5Eyov1lHqkTkVKT/I0I6aAwu+UnGgJXZugKQ9Xwm36PE5CAPIajWs+5bJYsWKue5CaNvz444/uPjWEUCqmn9DRKoNal2onej8matJHF/JKE/J7yGiPAwUOfrxVswg1nfDd53Qe0MSPXjfcajpMQYG6xilo8C2tWTUAsD8QKGST3W1prxxTpfYoQFDxmk4kSiPS0rW0bdvWdS7SicqnFilwCL+eagWUJqQuGAo8NJvlZ6VEbUqVMqT8Wr9Tp2+BqjqGPTnR+DZ9AJDsdJGd2aZhSu3USq4mU1S0rIt5FQmro5C/6BffHlqtoxUEhNuQKlDQSqwPJhQU6HU13nvacFITQuFVBdV8qV10uClEZnxLayZsAOwPBArZJLylvU4K/gLfn6S0hK1ZJ50stOysi3Zd9Ku1qD/BaHbJBw46SakwTichf4LQLJQCDL+5mWoHNLsVzpvVSalLly6xQEH087SUzYkGQCrRRbaf+PCbjfkxWcXL2gX57rvvdjVhGr+VyukDBd/AwY+3CgrURjocBGi1VrP/CiDUzUj1B1oRDq/0qpZBnZDCux4rTVRd7lRvAAA5iUAhm2andEGv4jMVrukEo8/V51onKS1XK/VHm+coNah9+/ZuPwNduPuTkvpta0UifFLSiU2dMfxKgPpu63NfGKcUJi1Ph3flVI9t9dr2qUQey9YA8vrO9GG6X+2gVSOgi/du3bq5VVqNydov5vnnn3c1BerspokUpRlp7Na4rfROXfSrK51WerXSoGYT6lik9E+t6sqLL77oUoo0jvvVYE3MaALITxZppVeppVoRDtMxMy4DyGm0R/0H/EDve2cnSsnRQK/HqV2d2pCqhalmibSsrBkjzVBpxklFcuEWeepGpIt8BQZaqtbeBOqUoR2NdVLSkrQ6XagDkZa1NVulx7Rr186duHQi09fhguf4YwofL6sJAJKdv7j2Y7I+hvc28I/ReDdt2jS7//77rWXLlq6eS5MuF110kWvsoK5DmsBRgXKYJmgmT57sxm0FBfXq1XOvozFYY7J2Nla6ksZfTcpodUEbXWql2BdGax+aRBuY+c3OPMZkALkBKwp7SRfYfpYnfBLSfepKpJQgBQB+Vl+DvWaPRowY4WakOnbs6PJdb7rpJjfTr/sUKGhWya8W+FUJzVgpvcinG2nmSwXO2llZ1J5PG6tpGVs/QzmymiGbMGFChhNRouJj6gwA5AXhMTl+0zONqbpQ174vGhf9Y0Sdgq699trY3jJK/9TFusZVUStTTbSEZ/Y1gaPaMb/SqwBDkzR+7NauycOHD3d1C08//bRbkdDrK7DQqoVobE60UsCO9AByIwKFEF2E+/zSzIqO/X4BOqGoFZ56Z+tiXhfvOhHpRKIThzZL83saaMMcpRxpKVuzV2p/p8JlBRXqpKEcVc1O+dQhXcTr9f/8809388ekrkZaUfDFclr27tGjh/v5OhadlHR88cdOUAAgWfgVz8zG4HjhPVx0Aa9VAo2HopQercgqDVOTNFrJFU2+aFxUSqjGUNUEHHjggS44UD2YaHzW6oFWe/3rr1692tWBaRwWNYdQmpJP79Qu85qwUUtU1Zip9sBP2oTfD6sFAJJFvoAkSEct7zQ7r/QfnTgS0UCvE42KkXViUF2BugbpZKMT0c033+wCAG2uo57bWqZ+6KGHXABwyy23uJalWtrWiUhL1jqpeK+88oqb+VKeau/evV2urFYoNDOlFQotZ/tgRkFHPAUWrBQAyEs05sanDsXTBblWcNXCVMXHfpJFqUAag5U+pNl6XbRr/Hz55Zdd8KDNKDW+KoVTY7LqEPwGaqLJH03saH8ajeN6Ta0WaBf74sWLu/1olN6piRo1kgjvnuxXcnV6ZaUAQFJToIAg2Llz5x59v02bNkHp0qWDtLS0YN26dcHWrVuDM844I6hSpUrw3XffxR7fu3fvoHbt2sGmTZuCmTNnBnXr1g2GDBmS4TVXr14djBkzxn2+ffv2YMKECUGDBg2CEiVKuOd+/PHHwbfffhvs2LHDPcZ/BIC8SuPl9ddfH5x00klBly5dgk8++STYtm1bpmPyww8/HBx88MFu7Jw1a5a776677goKFSoUPP7447HHT5s2LahWrVrw/vvvu7G7bdu2buwO0zj89ttvB4sWLXJfz549O2jRokVQoUKFoFixYsFDDz0UfPXVV27sFsZkAHldnk89SrR8nag7kWbjfS9rrRD45/rNbPzjtZ+BHtusWTNXW6Bl5c6dO7uOF77ThZx33nlulUIzVipqO+2002zIkCEub1UzVWqX98ADD7gahT/++MOtBCi/VUvm6enpbuZL+a+aqQoX5gFAXqQxVp3ftBO80is7dOjgxlSNi2pV6h/jOxnpJloJ0Gqt0ofUElqUZqSxU6lCnsZTjaFKG9LYrdVdrURoxUHjtH6mdjtWjYLf6Exd6JRSqlSiTZs2uTapajGtnyeMyQDyujw7yikHVUu+vugsUe9snXTU99pTt4uuXbu6lnb+JOA3s/HLx8pJ1fK09ifwFDQoyPAb74jqBbQ8rc4ZWpIeOnSoCzJGjRplTZo0cYXJarOnE4/vla1larUt1aZre5OjCwDJ3rZUY63qApSq+dJLL7lC40ceecTtQu93jvfFyhqPfe6/6rx0C0/+qBGEbupG5Hc81mSO0jZ1TlAAoTbU6hqnNE+Nzfo5GqeV/lm3bt3Ya5UvX94OO+ywDOlEAJAq8mygUL16dVdHoFqAMM3ea2diFQ8r9/Saa65xbUtFhWyakdL3RfsUqABZM1rqWPHf//7Xtc3T89RlyM9oqRhZNQrqkBEOIDTzpNoDf5/2L9D+CG+++aY7eWmW7Kyzzoqd8MIFbona+gFAXuAv9jXm+Y3OpE2bNm7m39MYq8kc1X7552l81j40GttV+6UJGbWSVlGx36BS9H0VIvvVAb/6oPFeLaTl1ltvdV3jHn/8cfv999/dOUO1an7FIJ6CGQqRAaSS/Hl5Ux1d3Pt0IH1P7fA0o6QiNO1OrJkrnURU5CaagdLMlG+Jp5OG0oXUulQt7tR1SCcqvYZOSDoJhVcVlE6kk42nZfTjjjvOzWR5Ckb8bNWuNmgDgLxKY6vGXc3Uq0vQuHHj3P1+ckSblmlmX00hREGFn5hR04hZs2a59KKrr77a3acJHK3qzps3L/YzFHAoeNDKracVC43Vmtzx1IVOY7tfyWXVAAD+p2Be3VRHFBBoSVk7YmoV4KSTTnK9stW1QjNSmsnXSoBWF3TiUUqQggCtAmjWSulD6qjh0478Rb1OQOp0oToCvyStVCItj6vjkXf55Zdnevx0KAKQitQ+WpM2Gn/vvvtuVzOgiRutHGgXZE3MqLW0VmQVDGhc1ViqiRetIGgltmjRohleU5Mv6kCkcVubTopqw/QzwisWek3VIWQ2JrOKCwBJGihopkcDefjmabMbta1TXYLShNRO1NcT6EShzW7U/k6zU2pJqpZ3Pt1H+x7o5KEiYgUKWoXQDNMHH3zgAoX4dCD/ugo+VJOg1CE/U6VbPAUX8QEBS9cAUpXSL7WioFowpXqqqFirrmo9rZUBTbqoQYQfN3Xhf++997oxXoFCfJAgmrDRayjo8GOuVg1UhBw//vrar3BQwJgMAInl6PSJZnH2NPVmbzbVUWchvyGZCtRUrCYqQlYu64cffhg7YajQWB2JfLFcjRo1XPrRp59+GjmB+M8VXKg7knJZ47HZGQBkThM3vkORT/FRmpFWErSCK4ku7rdt25bwfKHX0MSP0kS1M334ufo8fkym/gsAkmRFwaffKLc0frOaPd1URzsja1Md5bj6PFYVDSs9SLNMCgKmTp3qThY6EZ1yyimxrkb+wr9FixYu2FAnDF+YrJ+hImS10Ut0YlKwkAgnIADInNI71dVINC7rHKD0T7Uf1YSPKA1J47Wo+Fhjdrdu3RLO/Pv7Em1EKYzJALDvcnwEVeqOLv6Vk5qIn3FSDYFa1+kCfuTIka5ATYVsfnbK1xEo7Ug1CL5VqV5fj/FFxkpBUoqSipz9CUYpSTpJvffee7HZLbUxzSx4YZkaAPZN06ZNbdmyZTZ79uzY7L/Gb3UiUuqnUkk11msfhSOPPNLOPfdcl450/fXXc9EPANksR0dd7THgZ5CmTZuWITD4J5vq6EJehcZ6DT1WJyG/pK2VAJ1sVOjsHXzwwW5THZ2MRLUKOjmFuxUBAP45Tcyoy1CvXr3stddecw0g7rrrLqtUqVJsnxqN9WpAoW5zmth56KGHXLEyACB75dP2zPvrxf2mYfG9p32xWe/evW3t2rUuONBskuoE9PhEs0aa8b/00kvdc19//fXY/VoxOOigg1yhmwqVRUXFuk87H+ujTj6qVZg0aZJLc+revbtrkafXy4zvggEAyFpqQa06M60qrFq1yhUoqwOSiph318wCAJBHVhTCm+roAt1TkLB06VLXyk5t8dTGTqsL4UK1f7qpjnZZ9o+77LLL3AZsuvhXOpE210kUJISL3jghAcD+4duUqp5MY7VSkeKDhPDu9OFmFgCAPBIohDfV0Y6bflMd0YW9dknWCUNtSFW8puBBJwSlG2nHTLW229dNdbS0raVr6dmzp913330ZTjQ+pSmM/FcAyB4ajzWpo3Fd4jsa0Z0IAPJw16PMNtVRTUKPHj1csZpSj1QHoFUBtSXVhjoKHLQCoJ054/2TTXXi9zPwxc8AgOznJ27YgBIAcq+C2bmpji7o9bVODDopKD9VrUtV0KaCZHU+aty4sZUtWzbha/6TTXU4CQFA7kNKEQCkYDGzVg00868e2H7GSJuYDR482LVDVRci3a9C49atW7uVBqUb6WOi2X7/GupmpK4Y8R0wMiuCBgAAALD39tuVtTa/8Xsj+Nl9pQmtWLHCrQxoZ2PVFbzwwguuDqFVq1YufWjy5MkJXy+8qU6iNnkECQAAAEDWyZ+dm+oo9einn36ymjVrWq1atWI7b/q9DNLS0liGBgAAAPL6PgrawEyb6ai9qToRDRkyxO1l8Mwzz7gCZgAAAAApGCgk2lRHO2126tQp0+eobSkdiQAAAIA8HCj4+gRtqFOuXLlYv2wAAAAAKR4o+G5Fme1nAAAAACAFA4XMAgYAAAAAuVe29RQlSAAAAACSB5sPAAAAAIggUAAAAAAQQaAAAAAAIIJAAQAAAEAEgQIAAACACAIFAAAAABEECgAAAAAiCBQAAAAARBAoAAAAAIggUAAAAABg8f4fhmO1txbT9sIAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 800x500 with 1 Axes>"
       ]
@@ -928,7 +936,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -988,7 +996,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1020,7 +1028,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1056,21 +1064,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
+    "response_format = {\n",
+    "  \"name\": \"float_score_classification\",\n",
+    "  \"strict\": True,\n",
+    "  \"schema\": {\n",
+    "    \"type\": \"object\",\n",
+    "    \"properties\": {\n",
+    "      \"steps\": {\n",
+    "        \"type\": \"array\",\n",
+    "        \"description\": \"A sequence of steps outlining the reasoning process.\",\n",
+    "        \"items\": {\n",
+    "          \"type\": \"object\",\n",
+    "          \"properties\": {\n",
+    "            \"description\": {\n",
+    "              \"type\": \"string\",\n",
+    "              \"description\": \"Detailed description of the reasoning in this step.\"\n",
+    "            },\n",
+    "            \"conclusion\": {\n",
+    "              \"type\": \"string\",\n",
+    "              \"description\": \"The conclusion of the reasoning in this step.\"\n",
+    "            }\n",
+    "          },\n",
+    "          \"required\": [\"description\", \"conclusion\"],\n",
+    "          \"additionalProperties\": False\n",
+    "        }\n",
+    "      },\n",
+    "      \"result\": {\n",
+    "        \"type\": \"number\",\n",
+    "        \"description\": \"The float score assigned to the response. This should be in inclusive range RANGE_MIN to RANGE_MAX.\"\n",
+    "      }\n",
+    "    },\n",
+    "    \"required\": [\"steps\", \"result\"],\n",
+    "    \"additionalProperties\": False\n",
+    "  }\n",
+    "}\n",
     "\n",
-    "from pydantic import BaseModel\n",
-    "from typing import List\n",
-    "\n",
-    "class GraderStep(BaseModel):\n",
-    "    description: str\n",
-    "    conclusion: str\n",
-    "\n",
-    "class GraderResponse(BaseModel):\n",
-    "    result: float\n",
-    "    steps: List[GraderStep]\n",
+    "# for completions\n",
+    "response_format = {\n",
+    "    \"type\": \"json_schema\",\n",
+    "    \"json_schema\": response_format\n",
+    "}\n",
     "\n",
     "# Adapted python_model_grader to match the other graders' interface\n",
     "def python_model_grader(sample, item, model_grader=model_grader_1):\n",
@@ -1088,18 +1125,17 @@
     "        {\"role\": \"user\", \"content\": user_prompt_filled}\n",
     "    ]\n",
     "    # Call the OpenAI API with the grader's model\n",
-    "    response = client.beta.chat.completions.parse(\n",
+    "    response = client.chat.completions.create(\n",
     "        model=model_grader[\"model\"],\n",
     "        messages=messages,\n",
     "        seed=model_grader.get(\"sampling_params\", {}).get(\"seed\", None),\n",
     "        temperature=model_grader.get(\"sampling_params\", {}).get(\"temperature\", 0),\n",
-    "        response_format=GraderResponse,\n",
+    "        response_format=response_format,\n",
     "    )\n",
     "    # Parse the float score from the model's JSON response\n",
-    "    parsed = response.choices[0].message.parsed\n",
-    "    if not isinstance(parsed, GraderResponse):\n",
-    "        raise RuntimeError(f\"Grader returned invalid structured output: {parsed!r}\")\n",
-    "    return float(parsed.result)"
+    "    parsed = json.loads(response.choices[0].message.content)\n",
+    "    \n",
+    "    return float(parsed[\"result\"])"
    ]
   },
   {
@@ -1128,7 +1164,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1238,7 +1274,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1300,9 +1336,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Grader validated\n"
+     ]
+    }
+   ],
    "source": [
     "import requests\n",
     "\n",
@@ -1334,9 +1378,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training file detected: data/medical_01_verifiable_problem_train_simple_prompt.jsonl\n",
+      "Uploading file: data/medical_01_verifiable_problem_train_simple_prompt.jsonl\n",
+      "File uploaded successfully. File ID: file-19L9jKsJXNJ17DtjvPwN3M\n",
+      "test file detected: data/medical_01_verifiable_problem_val_simple_prompt.jsonl\n",
+      "Uploading file: data/medical_01_verifiable_problem_val_simple_prompt.jsonl\n",
+      "File uploaded successfully. File ID: file-78q2N1QAMKhLiRK3zVB6MC\n"
+     ]
+    }
+   ],
    "source": [
     "# Set your training and test file paths\n",
     "train_file = \"data/medical_01_verifiable_problem_train_simple_prompt.jsonl\"\n",
@@ -1371,7 +1428,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's now define the hyper-parameters for our run. We will be fine-tuning `o4-mini`, with the `medium` reasoning effort. This parameter will impact the length by limiting the number of tokens the model uses to reason. We tune with a moderate compute multiplier and reasonable number of epochs, prioritizing efficiency and fast iteration. You’ll want to tailor these depending on your budget, desired generalization, and dataset difficulty."
+    "Let's now define the hyper-parameters for our run. We will be fine-tuning `o4-mini`, with the `medium` reasoning effort. This parameter will impact the duration by limiting the number of tokens the model uses to reason. We tune with a moderate compute multiplier and reasonable number of epochs, prioritizing efficiency and fast iteration. Additionally, we set the `eval_samples` parameter to 3 to make the validation curves more robust given the stochasticity of `o4-mini`’s outputs. Averaging across multiple samples reduces noise and helps reveal consistent patterns of learning.\n",
+    "\n",
+    "You’ll want to tailor these depending on your budget, desired generalization, and dataset difficulty."
    ]
   },
   {
@@ -1387,9 +1446,9 @@
     "n_epochs = 5\n",
     "seed = 42\n",
     "grader = model_grader_2\n",
-    "response_format = None\n",
+    "response_format_predictions = None\n",
     "compute_multiplier = 1.0\n",
-    "eval_samples = 1\n",
+    "eval_samples = 3\n",
     "eval_interval = 5"
    ]
   },
@@ -1404,7 +1463,16 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training job created with ID: ftjob-tt3B7l45hLUoaXGJRfoL1lLT\n",
+      "View the job details at: https://platform.openai.com/finetune/ftjob-tt3B7l45hLUoaXGJRfoL1lLT\n"
+     ]
+    }
+   ],
    "source": [
     "# Launch the RFT job\n",
     "payload = dict(\n",
@@ -1416,7 +1484,7 @@
     "        type=\"reinforcement\",\n",
     "        reinforcement=dict(\n",
     "            grader=grader,\n",
-    "            response_format=response_format,\n",
+    "            response_format=response_format_predictions,\n",
     "            hyperparameters=dict(\n",
     "                compute_multiplier=compute_multiplier,\n",
     "                eval_samples=eval_samples,\n",
@@ -1511,55 +1579,54 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Generating predictions (run 1):   0%|          | 0/100 [00:00<?, ?it/s]"
+      "Generating predictions (run 1): 100%|██████████| 100/100 [01:16<00:00,  1.30it/s]\n",
+      "Generating predictions (run 2): 100%|██████████| 100/100 [01:25<00:00,  1.17it/s]\n",
+      "Generating predictions (run 3): 100%|██████████| 100/100 [01:07<00:00,  1.49it/s]\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Generating predictions (run 1): 100%|██████████| 100/100 [02:27<00:00,  1.47s/it]\n",
-      "Generating predictions (run 2): 100%|██████████| 100/100 [02:28<00:00,  1.49s/it]\n",
-      "Generating predictions (run 3): 100%|██████████| 100/100 [02:13<00:00,  1.33s/it]\n",
-      "Grading predictions: 100%|██████████| 100/100 [00:23<00:00,  4.30it/s]\n"
+      "Grading predictions: 100%|██████████| 100/100 [00:22<00:00,  4.51it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'total_samples': 100, 'accuracy': 0.7207700000000001}\n"
+      "{'total_samples': 100, 'accuracy': 0.7730899999999999}\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Grading predictions: 100%|██████████| 100/100 [00:29<00:00,  3.43it/s]\n"
+      "Grading predictions: 100%|██████████| 100/100 [00:17<00:00,  5.57it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'total_samples': 100, 'accuracy': 0.7125700000000001}\n"
+      "{'total_samples': 100, 'accuracy': 0.7697499999999999}\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Grading predictions: 100%|██████████| 100/100 [00:22<00:00,  4.39it/s]"
+      "Grading predictions: 100%|██████████| 100/100 [00:19<00:00,  5.01it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'total_samples': 100, 'accuracy': 0.7239800000000003}\n"
+      "{'total_samples': 100, 'accuracy': 0.78996}\n"
      ]
     },
     {
@@ -1587,6 +1654,7 @@
     "    reasoning_effort=reasoning_effort,\n",
     "    n_runs=num_runs\n",
     ")\n",
+    "\n",
     "run_prediction_evaluation(\n",
     "    model_name=model_name, \n",
     "    reasoning_effort=reasoning_effort, \n",
@@ -1607,58 +1675,59 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Results for run 1 already exist at data/rft/predictions/val_simple_o4-mini_medium_predictions_run1.json. Loading results.\n",
-      "Results for run 2 already exist at data/rft/predictions/val_simple_o4-mini_medium_predictions_run2.json. Loading results.\n",
-      "Results for run 3 already exist at data/rft/predictions/val_simple_o4-mini_medium_predictions_run3.json. Loading results.\n"
+      "Generating predictions (run 1):   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Grading predictions: 100%|██████████| 100/100 [00:21<00:00,  4.57it/s]\n"
+      "Generating predictions (run 1): 100%|██████████| 100/100 [01:11<00:00,  1.39it/s]\n",
+      "Generating predictions (run 2): 100%|██████████| 100/100 [00:42<00:00,  2.34it/s]\n",
+      "Generating predictions (run 3): 100%|██████████| 100/100 [00:41<00:00,  2.40it/s]\n",
+      "Grading predictions: 100%|██████████| 100/100 [00:19<00:00,  5.20it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'total_samples': 100, 'accuracy': 0.6749300000000003}\n"
+      "{'total_samples': 100, 'accuracy': 0.72282}\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Grading predictions: 100%|██████████| 100/100 [00:20<00:00,  4.96it/s]\n"
+      "Grading predictions: 100%|██████████| 100/100 [00:19<00:00,  5.14it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'total_samples': 100, 'accuracy': 0.6755199999999999}\n"
+      "{'total_samples': 100, 'accuracy': 0.72807}\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Grading predictions: 100%|██████████| 100/100 [00:24<00:00,  4.16it/s]"
+      "Grading predictions: 100%|██████████| 100/100 [00:17<00:00,  5.65it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'total_samples': 100, 'accuracy': 0.64916}\n"
+      "{'total_samples': 100, 'accuracy': 0.74812}\n"
      ]
     },
     {
@@ -1705,58 +1774,59 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Results for run 1 already exist at data/rft/predictions/val_simple_o3_medium_predictions_run1.json. Loading results.\n",
-      "Results for run 2 already exist at data/rft/predictions/val_simple_o3_medium_predictions_run2.json. Loading results.\n",
-      "Results for run 3 already exist at data/rft/predictions/val_simple_o3_medium_predictions_run3.json. Loading results.\n"
+      "Generating predictions (run 1):   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Grading predictions: 100%|██████████| 100/100 [00:32<00:00,  3.10it/s]\n"
+      "Generating predictions (run 1): 100%|██████████| 100/100 [01:01<00:00,  1.62it/s]\n",
+      "Generating predictions (run 2): 100%|██████████| 100/100 [00:52<00:00,  1.90it/s]\n",
+      "Generating predictions (run 3): 100%|██████████| 100/100 [01:13<00:00,  1.37it/s]\n",
+      "Grading predictions: 100%|██████████| 100/100 [00:21<00:00,  4.55it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'total_samples': 100, 'accuracy': 0.6493800000000001}\n"
+      "{'total_samples': 100, 'accuracy': 0.74015}\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Grading predictions: 100%|██████████| 100/100 [00:20<00:00,  4.89it/s]\n"
+      "Grading predictions: 100%|██████████| 100/100 [00:16<00:00,  6.08it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'total_samples': 100, 'accuracy': 0.6722}\n"
+      "{'total_samples': 100, 'accuracy': 0.7515900000000001}\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Grading predictions: 100%|██████████| 100/100 [00:20<00:00,  4.80it/s]"
+      "Grading predictions: 100%|██████████| 100/100 [00:16<00:00,  6.13it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'total_samples': 100, 'accuracy': 0.7137200000000001}\n"
+      "{'total_samples': 100, 'accuracy': 0.74235}\n"
      ]
     },
     {
@@ -1810,12 +1880,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x500 with 1 Axes>"
       ]
@@ -1843,7 +1913,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [
     {
@@ -1851,32 +1921,32 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "Total mistakes: 80\n",
+      "Total mistakes: 84\n",
       "\n",
-      "[Sample 5]\n",
-      "  Model prediction: carotid duplex ultrasound\n",
-      "  Reference answer: carotid doppler\n",
-      "  Score: 0.5525\n",
+      "[Sample 9]\n",
+      "  Model prediction: ventilation-perfusion scan\n",
+      "  Reference answer: lung ventilation-perfusion scan\n",
+      "  Score: 0.989\n",
       "\n",
-      "[Sample 6]\n",
-      "  Model prediction: under fixation due to insufficient fixation time\n",
-      "  Reference answer: incomplete fixation\n",
-      "  Score: 0.5037037037037037\n",
+      "[Sample 11]\n",
+      "  Model prediction: autoimmune destruction of melanocytes (vitiligo)\n",
+      "  Reference answer: autoimmune melanocyte destruction\n",
+      "  Score: 0.991\n",
       "\n",
-      "[Sample 7]\n",
-      "  Model prediction: acute rheumatic fever due to group a streptococcal pharyngitis mediated by type ii hypersensitivity\n",
-      "  Reference answer: acute rheumatic fever\n",
-      "  Score: 0.85\n",
+      "[Sample 12]\n",
+      "  Model prediction: contrast enhanced computed tomography of the abdomen\n",
+      "  Reference answer: ct abdomen\n",
+      "  Score: 0.812\n",
       "\n",
-      "[Sample 8]\n",
-      "  Model prediction: exposure (open) method of burn treatment\n",
-      "  Reference answer: heterograft application with sutures to secure it in place and daily washes, but no dressing\n",
-      "  Score: 0.3031007751937985\n",
+      "[Sample 13]\n",
+      "  Model prediction: unfractionated heparin\n",
+      "  Reference answer: enoxaparin\n",
+      "  Score: 0.428\n",
       "\n",
-      "[Sample 9]\n",
-      "  Model prediction: beta-lactamase production leading to enzymatic inactivation of ampicillin\n",
-      "  Reference answer: production of beta-lactamase enzyme\n",
-      "  Score: 0.7555555555555555\n"
+      "[Sample 15]\n",
+      "  Model prediction: t cell–mediated delayed (type iv) hypersensitivity\n",
+      "  Reference answer: th1-mediated cytotoxicity\n",
+      "  Score: 0.932\n"
      ]
     }
    ],
@@ -1900,26 +1970,26 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We see about a 5-point boost in accuracy after fine-tuning. Looking at the first few errors, the model tends to harshly penalize answers that are close but not clinically identical-like *carotid duplex ultrasound* vs. *carotid doppler*. It also dings longer answers, even when they’re correct, like *beta-lactamase production leading to enzymatic inactivation of ampicillin*."
+    "We see about a 5-point boost in accuracy after fine-tuning. Looking at the first few errors, the model tends to harshly penalize answers that are close but not clinically identical-like *unfractionated heparin* vs. *enoxaparin*. It also dings longer answers, even when they’re correct, like *contrast enhanced computed tomography of the abdomen*."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "o4-mini-medium-simple-prompt bin counts: [ 4. 15.  9.  7.  7.  4.  3.  5. 22. 24.]\n",
-      "ftmodel-medium-simple-prompt bin counts: [ 8. 15.  7.  3.  9.  7.  8.  4. 19. 20.]\n",
-      "Max bin count (y-axis): 24.0\n"
+      "o4-mini-medium-simple-prompt bin counts: [ 2. 20. 13.  5. 60.]\n",
+      "ftmodel-medium-simple-prompt bin counts: [ 3. 12.  9.  6. 70.]\n",
+      "Max bin count (y-axis): 70.0\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1934,7 +2004,7 @@
     "\n",
     "# Determine common bins for both histograms\n",
     "all_scores = scores_o4 + scores_ft\n",
-    "bins = plt.hist(all_scores, bins=10, alpha=0)[1]\n",
+    "bins = plt.hist(all_scores, bins=5, alpha=0)[1]\n",
     "\n",
     "# Plot histograms and capture the counts\n",
     "counts_o4, _, _ = plt.hist(\n",
@@ -1953,7 +2023,7 @@
     "plt.title(\"Model Grader 2 Score Distribution by Model\")\n",
     "plt.xlabel(\"Score\")\n",
     "plt.ylabel(\"Count\")\n",
-    "plt.ylim(top=25)\n",
+    "plt.ylim(top=75)\n",
     "plt.legend()\n",
     "\n",
     "# Print the bin counts\n",
@@ -1966,7 +2036,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Looking at the distruibution of scores, we observe that RFT helped shift the model’s predictions out of the mid-to-low score zone (0.4–0.5) and into the mid-to-high range (0.5–0.6). Since the grader emphasizes clinical similarity over lexical match, this shift reflects stronger medical reasoning-not just better phrasing-according to our *expert* grader. As observed in the 0.9-1.0 range, some verbosity crept in despite mitigations and slightly lowering scores throughout, though it often reflected more complete, semantically aligned answers. A future grader pass could better account for these cases.\n",
+    "Looking at the distruibution of scores, we observe that RFT helped shift the model’s predictions out of the mid-to-low score zone (0.2-0.6) and into the high range (0.8-1.0). Since the grader emphasizes clinical similarity over lexical match, this shift reflects stronger medical reasoning-not just better phrasing-according to our *expert* grader. As seen in the (0.0-0.1) range, a handful of already weak predictions fell even further, hinting at a residual knowledge gap.\n",
     "\n",
     "Note that, because the earlier `combined_grader` was designed to reward lexical correctness, its accuracy didnʼt improve much-which is expected. That gap reinforces why validating your model grader is critical, and why you should monitor for reward-hacking. In our case, we used `o3` to spot-check grading behavior, but domain expert review is essential. "
    ]
@@ -1982,16 +2052,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 83,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean reasoning_tokens_used o4-mini: 424\n",
-      "Mean reasoning_tokens_used o3: 353\n",
-      "Mean reasoning_tokens_used ftmodel: 1820\n"
+      "Mean reasoning_tokens_used o4-mini: 404\n",
+      "Mean reasoning_tokens_used o3: 384\n",
+      "Mean reasoning_tokens_used ftmodel: 925\n"
      ]
     }
    ],
@@ -2019,46 +2089,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Classifying staging type\n",
+      "**Choosing imaging study**\n",
       "\n",
-      "The user provided a clinical scenario of a 35-year-old female with a 5 cm oral tumor and a 2 cm lymph node. They're asking how to stage it according to the TNM classification. This is a diagnosis query, so the correct answer type here is \"diagnosis.\" Considering the tumor's size, it appears to be classified as T3 since it's greater than 4 cm. Thus, I think the staging might be Stage II, but I'll confirm that.\n"
+      "The user is looking for a single phrase regarding the imaging study for a 49-year-old male with chronic alcohol consumption and related symptoms. I'm considering whether to suggest a CT scan or MRI; however, a CT scan is often the initial choice for chronic pancreatitis. I’ll go with \"abdominal ct scan\" since it's standardized. I need to ensure I format it in lowercase without punctuation, following the user’s request. So the output is \"abdominal ct scan.\"\n"
      ]
     }
    ],
    "source": [
     "from IPython.display import Markdown, display\n",
-    "markdown_text = results_o4mini_model_grader_2[5][\"summaries\"]\n",
+    "markdown_text = results_o4mini_model_grader_2[0][30][\"summaries\"]\n",
     "display(Markdown(markdown_text))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Clarifying T staging for cancers\n",
-      "\n",
-      "I’m digging into T staging for head and neck cancers in the oral cavity. So, T1 applies to tumors 2 cm or less, T2 for those over 2 cm but not more than 4 cm, and T3 is for tumors over 4 cm. T4a indicates invasion into adjacent structures. The patient's tumor measures 5 cm, which is over 4 cm. I’m not sure if it fits T3 or T4a, since T4a involves additional invasiveness, not just size. Determining T and N staging\n",
+      "**Considering imaging options**\n",
       "\n",
-      "I’m looking at a 5 cm tumor in the oral cavity. It seems there’s no mention of invasion into adjacent structures, so I’m categorizing it as T3 due to its size. T4a usually means invasion into structures like bone or skin. According to the TNM classification, since I see no such invasion, T classification remains T3.\n",
+      "I'm analyzing the user's question about a 49-year-old male with symptoms suggesting steatorrhea, possibly indicating exocrine pancreatic insufficiency from chronic alcohol use. It raises concerns about chronic pancreatitis or pancreatic cancer. I think the best imaging choice is a contrast-enhanced CT scan of the abdomen because it effectively examines structural abnormalities. Alternatively, an endoscopic ultrasound could be more sensitive, but CT is generally preferred. So, my recommendation is to start with a contrast-enhanced CT scan.\n",
+      "**Determining the appropriate imaging study**\n",
       "\n",
-      "Moving on to N staging, I see there's a single lymph node of 2 cm on the same side; this fits the N1 classification for metastasis, as it’s less than 3 cm.\n"
+      "I'm analyzing the question about the most suitable imaging study for a patient with symptoms suggesting chronic pancreatitis. The standard approach for suspected chronic pancreatitis is a contrast-enhanced CT scan of the abdomen, as it effectively identifies pancreatic calcifications and structural changes. While MRCP and endoscopic ultrasound provide additional details, CT is often preferred as the initial test. Therefore, my answer should focus on recommending a \"contrast-enhanced abdominal CT\" as the next step in evaluation.\n"
      ]
     }
    ],
    "source": [
-    "markdown_text = results_ft_model_grader_2[5][\"summaries\"]\n",
+    "markdown_text = results_ft_model_grader_2[0][30][\"summaries\"]\n",
     "display(Markdown(markdown_text))"
    ]
   },
@@ -2066,7 +2135,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Base `o4-mini`'s reasoning gives a quick answer but doesn’t explain how it got there. It mentions the tumor size but doesn’t walk through the actual TNM rules, and it seems unsure about the result. On the other hand, the `finetuned model` is more thoughtful - breaking down the T and N staging step by step and explaining why each part applies. The latter seems more careful, and seems to have learnt to break down the case description even more."
+    "Base `o4‑mini`’s reasoning zooms straight to “abdominal CT scan,” mostly worrying about lowercase formatting and giving only a cursory “often the initial choice” justification. The `finetuned model`, meanwhile, first links the patient’s steatorrhea and alcohol history to chronic pancreatitis or cancer, weighs CT against MRCP and EUS, and explains why a contrast‑enhanced abdominal CT best reveals calcifications and structural change. The latter seems more careful, and seems to have learnt to break down the case description even more."
    ]
   },
   {
diff --git a/images/rft_dashboard_modelgrader2.png b/images/rft_dashboard_modelgrader2.png
index 731c38c9af..ad9213d64d 100644
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