diff --git a/week6/community-contributions/bharat_puri/fine_tuned_concept.ipynb b/week6/community-contributions/bharat_puri/fine_tuned_concept.ipynb
index 78d4a03..c87522d 100644
--- a/week6/community-contributions/bharat_puri/fine_tuned_concept.ipynb
+++ b/week6/community-contributions/bharat_puri/fine_tuned_concept.ipynb
@@ -58,18 +58,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "4dd3aad2-6f99-433c-8792-e461d2f06622",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Note: Environment variable`HF_TOKEN` is set and is the current active token independently from the token you've just configured.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Log in to HuggingFace\n",
     "\n",
@@ -79,19 +71,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "884a50bd-8cae-425e-8e56-f079fc3e65ce",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Input columns: [\"How much does this cost to the nearest dollar?\\n\\nOEM AC Compressor w/A/C Repair Kit For Ford F150 F-150 V8 & Lincoln Mark LT 2007 2008 - BuyAutoParts NEW\\nAs one of the world's largest automotive parts suppliers, our parts are trusted every day by mechanics and vehicle owners worldwide. This A/C Compressor and Components Kit is manufactured and tested to the strictest OE standards for unparalleled performance. Built for trouble-free ownership and 100% visually inspected and quality tested, this A/C Compressor and Components Kit is backed by our 100% satisfaction guarantee. Guaranteed Exact Fit for easy installation 100% BRAND NEW, premium ISO/TS 16949 quality - tested to meet or exceed OEM specifications Engineered for superior durability, backed by industry-leading unlimited-mileage warranty Included in this K\\n\\nPrice is $\", '0']\n",
-      "Output columns: [\"How much does this cost to the nearest dollar?\\n\\nOEM AC Compressor w/A/C Repair Kit For Ford F150 F-150 V8 & Lincoln Mark LT 2007 2008 - BuyAutoParts NEW\\nAs one of the world's largest automotive parts suppliers, our parts are trusted every day by mechanics and vehicle owners worldwide. This A/C Compressor and Components Kit is manufactured and tested to the strictest OE standards for unparalleled performance. Built for trouble-free ownership and 100% visually inspected and quality tested, this A/C Compressor and Components Kit is backed by our 100% satisfaction guarantee. Guaranteed Exact Fit for easy installation 100% BRAND NEW, premium ISO/TS 16949 quality - tested to meet or exceed OEM specifications Engineered for superior durability, backed by industry-leading unlimited-mileage warranty Included in this K\\n\\nPrice is $\", '120']\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# =============================================\n",
     "# Step 1 – Load and Inspect Dataset (CSV files)\n",
@@ -115,19 +98,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "b0a6fb86-74a4-403c-ab25-6db2d74e9d2b",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training samples: 199 | Validation samples: 50\n",
-      "✅ Train and validation data prepared successfully.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# =============================================\n",
     "# Step 2 – Split into Train and Validation Sets\n",
@@ -149,72 +123,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "c830ed3e-24ee-4af6-a07b-a1bfdcd39278",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>prompt</th>\n",
-       "      <th>completion</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>137</th>\n",
-       "      <td>How much does this cost to the nearest dollar?...</td>\n",
-       "      <td>How much does this cost to the nearest dollar?...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>How much does this cost to the nearest dollar?...</td>\n",
-       "      <td>How much does this cost to the nearest dollar?...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>97</th>\n",
-       "      <td>How much does this cost to the nearest dollar?...</td>\n",
-       "      <td>How much does this cost to the nearest dollar?...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                prompt  \\\n",
-       "137  How much does this cost to the nearest dollar?...   \n",
-       "6    How much does this cost to the nearest dollar?...   \n",
-       "97   How much does this cost to the nearest dollar?...   \n",
-       "\n",
-       "                                            completion  \n",
-       "137  How much does this cost to the nearest dollar?...  \n",
-       "6    How much does this cost to the nearest dollar?...  \n",
-       "97   How much does this cost to the nearest dollar?...  "
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "train_df.head(3)\n",
     "val_df.head(3)\n"
@@ -260,21 +172,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "8ae2fb3c-1cff-4ce3-911e-627c970edd7b",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "⚙️ Simulating fine-tuning process (no API cost)...\n",
-      "Epoch 1/1 training...\n",
-      "Fine-tuning complete ✅ (simulated)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# =============================================\n",
     "# Step 4 – Launch Fine-Tuning Job or Simulate\n",
@@ -315,22 +216,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "1aa280f6-1227-426a-a2e2-1ce985feba1e",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "🔍 Evaluating model performance...\n",
-      "✅ Simulation mode: generated random prediction values for evaluation.\n",
-      "\n",
-      "📊 Validation Mean Absolute Error (MAE): 3.30\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# =============================================\n",
     "# Step 5 – Evaluate Fine-Tuned (or Simulated) Model\n",
@@ -380,34 +269,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "c0e5b56c-8a0b-4d8e-a112-ce87efb4e152",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 600x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "===== WEEK 6 REFLECTION =====\n",
-      "✅ Completed the full fine-tuning workflow successfully.\n",
-      "🧠 Simulation mode enabled full understanding without any API cost.\n",
-      "📊 Validation MAE: 3.30 (simulated)\n",
-      "🔍 Learned how to prepare data, configure fine-tuning, and evaluate models safely.\n",
-      "💡 Next step: Try real fine-tuning (simulate=False) on small data if free credits are available.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# =============================================\n",
     "# Step 6 – Visualize and Reflect (Fixed)\n",
diff --git a/week6/community-contributions/bharat_puri/fine_tuned_simulation.ipynb b/week6/community-contributions/bharat_puri/fine_tuned_simulation.ipynb
index 3770d6f..288dceb 100644
--- a/week6/community-contributions/bharat_puri/fine_tuned_simulation.ipynb
+++ b/week6/community-contributions/bharat_puri/fine_tuned_simulation.ipynb
@@ -58,18 +58,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "4dd3aad2-6f99-433c-8792-e461d2f06622",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Note: Environment variable`HF_TOKEN` is set and is the current active token independently from the token you've just configured.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Log in to HuggingFace\n",
     "\n",
@@ -79,32 +71,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "id": "9c69e347-91bc-4eb1-843f-a17ed485667c",
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "🔍 Starting data curation...\n",
-      "✅ Cleaned dataset shape: (249, 2)\n",
-      "                                              prompt  \\\n",
-      "0  How much does this cost to the nearest dollar?...   \n",
-      "1  How much does this cost to the nearest dollar?...   \n",
-      "2  How much does this cost to the nearest dollar?...   \n",
-      "\n",
-      "                                          completion  \n",
-      "0  How much does this cost to the nearest dollar?...  \n",
-      "1  How much does this cost to the nearest dollar?...  \n",
-      "2  How much does this cost to the nearest dollar?...  \n",
-      "Training samples: 224, Validation samples: 25\n",
-      "💾 Saved train.pkl and test.pkl successfully.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# =============================================================\n",
     "# Step 1 — Data Curation and Preparation (Integrated from 09_part1_data_curation)\n",
@@ -152,20 +124,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "id": "b0a6fb86-74a4-403c-ab25-6db2d74e9d2b",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "📦 Loading curated train/test data from pickle files...\n",
-      "✅ Loaded train=224 | val=25\n",
-      "💾 Saved train.jsonl and val.jsonl for fine-tuning.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# =============================================================\n",
     "# Step 2 — Prepare Data for Fine-Tuning\n",
@@ -195,25 +157,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "id": "c830ed3e-24ee-4af6-a07b-a1bfdcd39278",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "⚙️ Fine-tuning configuration:\n",
-      "{\n",
-      "  \"model\": \"gpt-4o-mini\",\n",
-      "  \"n_epochs\": 3,\n",
-      "  \"batch_size\": 8,\n",
-      "  \"learning_rate_multiplier\": 0.5,\n",
-      "  \"suffix\": \"week6_bharat_ft_v1\"\n",
-      "}\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# =============================================================\n",
     "# Step 3 — Fine-Tuning Configuration\n",
@@ -258,23 +205,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "id": "e8367135-f40e-43e1-8f3c-09e990ab1194",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "🧪 Simulation mode — running mock fine-tuning steps...\n",
-      "Simulated Epoch 1/3\n",
-      "Simulated Epoch 2/3\n",
-      "Simulated Epoch 3/3\n",
-      "✅ Simulation complete — no API cost.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# =============================================================\n",
     "# Step 4 — Launch Fine-Tuning Job (Fixed for latest SDK)\n",
@@ -332,44 +266,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": null,
    "id": "32a2b85e-e978-4c8f-90d9-d697731e6569",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "🧮 Evaluating simulated fine-tuned model performance...\n",
-      "\n",
-      "📊 Validation Mean Absolute Error (Simulated): 1.76\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 600x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "===== WEEK 6 REFLECTION =====\n",
-      "✅ Completed full fine-tuning workflow (simulated) successfully.\n",
-      "🧠 Understood how fine-tuning integrates with GPT-4o-mini API workflow.\n",
-      "📊 Validation MAE (simulated): 1.76\n",
-      "🔍 Practiced prompt alignment, data curation, and evaluation safely.\n",
-      "💡 Next step: Try real fine-tuning (simulate=False) on small data if credits are available.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# =============================================================\n",
     "# Step 5 — Evaluate Simulated Fine-Tuned Model\n",