From 7084123d10ae7faef13241481cff634131c193d8 Mon Sep 17 00:00:00 2001
From: Sebastian Raschka <mail@sebastianraschka.com>
Date: Wed, 1 Oct 2025 10:47:04 -0500
Subject: [PATCH] Note about output dimensions (#862)

---
 ch03/01_main-chapter-code/ch03.ipynb | 28 ++++++++++++++++++++++++++--
 1 file changed, 26 insertions(+), 2 deletions(-)

diff --git a/ch03/01_main-chapter-code/ch03.ipynb b/ch03/01_main-chapter-code/ch03.ipynb
index 46c82b8..c01d0f2 100644
--- a/ch03/01_main-chapter-code/ch03.ipynb
+++ b/ch03/01_main-chapter-code/ch03.ipynb
@@ -1900,8 +1900,32 @@
    "metadata": {},
    "source": [
     "- Note that the above is essentially a rewritten version of `MultiHeadAttentionWrapper` that is more efficient\n",
-    "- The resulting output looks a bit different since the random weight initializations differ, but both are fully functional implementations that can be used in the GPT class we will implement in the upcoming chapters\n",
-    "- Note that in addition, we added a linear projection layer (`self.out_proj `) to the `MultiHeadAttention` class above. This is simply a linear transformation that doesn't change the dimensions. It's a standard convention to use such a projection layer in LLM implementation, but it's not strictly necessary (recent research has shown that it can be removed without affecting the modeling performance; see the further reading section at the end of this chapter)\n"
+    "- The resulting output looks a bit different since the random weight initializations differ, but both are fully functional implementations that can be used in the GPT class we will implement in the upcoming chapters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c8bd41e1-32d4-4067-a6d0-fe756a6511a9",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "**A note about the output dimensions**\n",
+    "\n",
+    "- In the `MultiHeadAttention` above, I used `d_out=2` to use the same setting as in the `MultiHeadAttentionWrapper` class earlier\n",
+    "- The `MultiHeadAttentionWrapper`, due the the concatenation, returns the output head dimension `d_out * num_heads` (i.e., `2*2 = 4`)\n",
+    "- However, the `MultiHeadAttention` class (to make it more user-friendly) allows us to control the output head dimension directly via `d_out`; this means, if we set `d_out = 2`, the output head dimension will be 2, regardless of the number of heads\n",
+    "- In hindsight, as readers [pointed out](https://github.com/rasbt/LLMs-from-scratch/pull/859), it may be more intuitive to use `MultiHeadAttention` with `d_out = 4` so that it produces the same output dimensions as `MultiHeadAttentionWrapper` with `d_out = 2`.\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9310bfa5-9aa9-40b4-8081-a5d8db5faf74",
+   "metadata": {},
+   "source": [
+    "- Note that in addition, we added a linear projection layer (`self.out_proj `) to the `MultiHeadAttention` class above. This is simply a linear transformation that doesn't change the dimensions. It's a standard convention to use such a projection layer in LLM implementation, but it's not strictly necessary (recent research has shown that it can be removed without affecting the modeling performance; see the further reading section at the end of this chapter)"
    ]
   },
   {