diff --git a/ch05/07_gpt_to_llama/converting-gpt-to-llama2.ipynb b/ch05/07_gpt_to_llama/converting-gpt-to-llama2.ipynb
index e75ff4a6..00bcabbb 100644
--- a/ch05/07_gpt_to_llama/converting-gpt-to-llama2.ipynb
+++ b/ch05/07_gpt_to_llama/converting-gpt-to-llama2.ipynb
@@ -381,7 +381,7 @@
     "id": "qcD8LSHNhBRW"
    },
    "source": [
-    "- Note that we also added a `dtype=cfg[\"dtype\"]` setting above, which will allow us to load the model directly in lower precision formats later to save memory (versus instantiating it in the original 32-bit precision format and then converting it)\n",
+    "- Note that we also added a `dtype=cfg[\"dtype\"]` setting above, which will allow us to load the model directly in lower precision formats later to reduce memory usage (versus instantiating it in the original 32-bit precision format and then converting it)\n",
     "- We also set `bias=False` since Llama doesn't use any bias units"
    ]
   },
@@ -648,7 +648,7 @@
     "\n",
     "mha(example_batch)\n",
     "\n",
-    "del mha  # delete to safe memory"
+    "del mha  # delete to free up memory"
    ]
   },
   {
@@ -781,7 +781,7 @@
     "        self.out_head = nn.Linear(cfg[\"emb_dim\"], cfg[\"vocab_size\"], bias=False, dtype=cfg[\"dtype\"])\n",
     "\n",
     "    def forward(self, in_idx):\n",
-    "        batch_size, seq_len = in_idx.shape\n",
+    "        # batch_size, seq_len = in_idx.shape\n",
     "        tok_embeds = self.tok_emb(in_idx)\n",
     "        # pos_embeds = self.pos_emb(torch.arange(seq_len, device=in_idx.device))\n",
     "        x = tok_embeds  # + pos_embeds  # Shape [batch_size, num_tokens, emb_size]\n",
@@ -890,7 +890,7 @@
     "    \"n_heads\": 32,           # Number of attention heads\n",
     "    \"n_layers\": 32,          # Number of layers\n",
     "    \"hidden_dim\": 11008,     # NEW: Size of the intermediate dimension in FeedForward\n",
-    "    \"dtype\": torch.bfloat16  # NEW: Lower-precision dtype to save memory\n",
+    "    \"dtype\": torch.bfloat16  # NEW: Lower-precision dtype to reduce memory usage\n",
     "}"
    ]
   },
@@ -1691,7 +1691,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.11.4"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {
diff --git a/ch05/07_gpt_to_llama/converting-llama2-to-llama3.ipynb b/ch05/07_gpt_to_llama/converting-llama2-to-llama3.ipynb
index eac9d582..4e211ba0 100644
--- a/ch05/07_gpt_to_llama/converting-llama2-to-llama3.ipynb
+++ b/ch05/07_gpt_to_llama/converting-llama2-to-llama3.ipynb
@@ -481,7 +481,7 @@
     "        ):\n",
     "        super().__init__()\n",
     "        assert d_out % num_heads == 0, \"d_out must be divisible by num_heads\"\n",
-    "        assert num_heads % num_kv_groups == 0, \"num_heads must be divisible by num_kv_groups\"\n",
+    "        assert num_heads % num_kv_groups == 0, \"num_heads must be divisible by num_kv_groups\"  # NEW\n",
     "\n",
     "        self.d_out = d_out\n",
     "        self.num_heads = num_heads\n",
@@ -886,7 +886,7 @@
     "    \"n_heads\": 32,           # Number of attention heads\n",
     "    \"n_layers\": 32,          # Number of layers\n",
     "    \"hidden_dim\": 11_008,    # Size of the intermediate dimension in FeedForward\n",
-    "    \"dtype\": torch.bfloat16  # Lower-precision dtype to save memory\n",
+    "    \"dtype\": torch.bfloat16  # Lower-precision dtype to reduce memory usage\n",
     "}"
    ]
   },
@@ -909,7 +909,7 @@
     "    \"n_kv_groups\": 8,        # NEW: Key-Value groups for grouped-query attention\n",
     "    \"rope_base\": 500_000.0,  # NEW: The base in RoPE's \"theta\" was increased to 500_000\n",
     "    \"rope_freq\": None,       # NEW: Additional configuration for adjusting the RoPE frequencies\n",
-    "    \"dtype\": torch.bfloat16  # Lower-precision dtype to save memory\n",
+    "    \"dtype\": torch.bfloat16  # Lower-precision dtype to reduce memory usage\n",
     "}"
    ]
   },
@@ -2062,7 +2062,7 @@
     "    \"n_kv_groups\": 8,        # Key-Value groups for grouped-query attention\n",
     "    \"rope_base\": 500_000.0,  # The base in RoPE's \"theta\"\n",
     "    \"rope_freq\": None,       # Additional configuration for adjusting the RoPE frequencies\n",
-    "    \"dtype\": torch.bfloat16  # Lower-precision dtype to save memory\n",
+    "    \"dtype\": torch.bfloat16  # Lower-precision dtype to reduce memory usage\n",
     "}\n",
     "\n",
     "LLAMA31_CONFIG_8B = {\n",
@@ -2074,7 +2074,7 @@
     "    \"hidden_dim\": 14_336,       # Size of the intermediate dimension in FeedForward\n",
     "    \"n_kv_groups\": 8,           # Key-Value groups for grouped-query attention\n",
     "    \"rope_base\": 500_000.0,     # The base in RoPE's \"theta\"\n",
-    "    \"dtype\": torch.bfloat16,    # Lower-precision dtype to save memory\n",
+    "    \"dtype\": torch.bfloat16,    # Lower-precision dtype to reduce memory usage\n",
     "    \"rope_freq\": {              # NEW: RoPE frequency scaling\n",
     "        \"factor\": 8.0,\n",
     "        \"low_freq_factor\": 1.0,\n",
@@ -2448,7 +2448,7 @@
     "    \"hidden_dim\": 14_336,       # Size of the intermediate dimension in FeedForward\n",
     "    \"n_kv_groups\": 8,           # Key-Value groups for grouped-query attention\n",
     "    \"rope_base\": 500_000.0,     # The base in RoPE's \"theta\"\n",
-    "    \"dtype\": torch.bfloat16,    # Lower-precision dtype to save memory\n",
+    "    \"dtype\": torch.bfloat16,    # Lower-precision dtype to reduce memory usagey\n",
     "    \"rope_freq\": {              # NEW: RoPE frequency scaling\n",
     "        \"factor\": 8.0,\n",
     "        \"low_freq_factor\": 1.0,\n",
@@ -2467,7 +2467,7 @@
     "    \"hidden_dim\": 8192,         # NEW: Almost half the size of the intermediate dimension in FeedForward\n",
     "    \"n_kv_groups\": 8,           # Key-Value groups for grouped-query attention\n",
     "    \"rope_base\": 500_000.0,     # The base in RoPE's \"theta\"\n",
-    "    \"dtype\": torch.bfloat16,    # Lower-precision dtype to save memory\n",
+    "    \"dtype\": torch.bfloat16,    # Lower-precision dtype to reduce memory usage\n",
     "    \"rope_freq\": {              # RoPE frequency scaling\n",
     "        \"factor\": 32.0,         # NEW: Adjustment of the rescaling factor\n",
     "        \"low_freq_factor\": 1.0,\n",
diff --git a/ch05/07_gpt_to_llama/standalone-llama32.ipynb b/ch05/07_gpt_to_llama/standalone-llama32.ipynb
index e4c94c41..d108df3a 100644
--- a/ch05/07_gpt_to_llama/standalone-llama32.ipynb
+++ b/ch05/07_gpt_to_llama/standalone-llama32.ipynb
@@ -438,7 +438,7 @@
     "    \"hidden_dim\": 8192,         # Size of the intermediate dimension in FeedForward\n",
     "    \"n_kv_groups\": 8,           # Key-Value groups for grouped-query attention\n",
     "    \"rope_base\": 500_000.0,     # The base in RoPE's \"theta\"\n",
-    "    \"dtype\": torch.bfloat16,    # Lower-precision dtype to save memory\n",
+    "    \"dtype\": torch.bfloat16,    # Lower-precision dtype to reduce memory usage\n",
     "    \"rope_freq\": {              # RoPE frequency scaling\n",
     "        \"factor\": 32.0,\n",
     "        \"low_freq_factor\": 1.0,\n",
@@ -458,7 +458,7 @@
     "#     \"hidden_dim\": 8192,         # Size of the intermediate dimension in FeedForward\n",
     "#     \"n_kv_groups\": 8,           # Key-Value groups for grouped-query attention\n",
     "#     \"rope_base\": 500_000.0,     # The base in RoPE's \"theta\"\n",
-    "#     \"dtype\": torch.bfloat16,    # Lower-precision dtype to save memory\n",
+    "#     \"dtype\": torch.bfloat16,    # Lower-precision dtype to reduce memory usage\n",
     "#     \"rope_freq\": {              # RoPE frequency scaling\n",
     "#         \"factor\": 32.0,\n",
     "#         \"low_freq_factor\": 1.0,\n",