diff --git a/ch04/01_main-chapter-code/exercise-solutions.ipynb b/ch04/01_main-chapter-code/exercise-solutions.ipynb
index 9bc2e62..5e4c49a 100644
--- a/ch04/01_main-chapter-code/exercise-solutions.ipynb
+++ b/ch04/01_main-chapter-code/exercise-solutions.ipynb
@@ -62,7 +62,33 @@
    "execution_count": 2,
    "id": "2751b0e5-ffd3-4be2-8db3-e20dd4d61d69",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TransformerBlock(\n",
+      "  (att): MultiHeadAttention(\n",
+      "    (W_query): Linear(in_features=768, out_features=768, bias=False)\n",
+      "    (W_key): Linear(in_features=768, out_features=768, bias=False)\n",
+      "    (W_value): Linear(in_features=768, out_features=768, bias=False)\n",
+      "    (out_proj): Linear(in_features=768, out_features=768, bias=True)\n",
+      "    (dropout): Dropout(p=0.1, inplace=False)\n",
+      "  )\n",
+      "  (ff): FeedForward(\n",
+      "    (layers): Sequential(\n",
+      "      (0): Linear(in_features=768, out_features=3072, bias=True)\n",
+      "      (1): GELU()\n",
+      "      (2): Linear(in_features=3072, out_features=768, bias=True)\n",
+      "    )\n",
+      "  )\n",
+      "  (norm1): LayerNorm()\n",
+      "  (norm2): LayerNorm()\n",
+      "  (drop_shortcut): Dropout(p=0.1, inplace=False)\n",
+      ")\n"
+     ]
+    }
+   ],
    "source": [
     "from gpt import TransformerBlock\n",
     "\n",
@@ -76,7 +102,8 @@
     "    \"qkv_bias\": False\n",
     "}\n",
     "\n",
-    "block = TransformerBlock(GPT_CONFIG_124M)"
+    "block = TransformerBlock(GPT_CONFIG_124M)\n",
+    "print(block)"
    ]
   },
   {
@@ -126,6 +153,31 @@
     "- Optionally multiply by 12 to capture all transformer blocks in the 124M GPT model"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "597e9251-e0a9-4972-8df6-f280f35939f9",
+   "metadata": {},
+   "source": [
+    "**Bonus: Mathematical breakdown**\n",
+    "\n",
+    "- For those interested in how these parameter counts are calculated mathematically, you can find the breakdown below (assuming `emb_dim=768`):\n",
+    "\n",
+    "\n",
+    "Feed forward module:\n",
+    "\n",
+    "- 1st `Linear` layer: 768 inputs × 4×768 outputs + 4×768 bias units = 2,362,368\n",
+    "- 2nd `Linear` layer: 4×768 inputs × 768 outputs + 768 bias units = 2,360,064\n",
+    "- Total:  1st `Linear` layer + 2nd `Linear` layer = 2,362,368 + 2,360,064 = 4,722,432\n",
+    "\n",
+    "Attention module:\n",
+    "\n",
+    "- `W_query`: 768 inputs × 768 outputs = 589,824 \n",
+    "- `W_key`: 768 inputs × 768 outputs = 589,824\n",
+    "- `W_value`: 768 inputs × 768 outputs = 589,824 \n",
+    "- `out_proj`: 768 inputs × 768 outputs + 768 bias units = 590,592\n",
+    "- Total: `W_query` + `W_key` + `W_value` + `out_proj` = 3×589,824 + 590,592 = 2,360,064 "
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "0f7b7c7f-0fa1-4d30-ab44-e499edd55b6d",