diff --git a/ch05/11_qwen3/README.md b/ch05/11_qwen3/README.md
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--- a/ch05/11_qwen3/README.md
+++ b/ch05/11_qwen3/README.md
@@ -9,6 +9,22 @@ This [standalone-qwen3-moe.ipynb](standalone-qwen3-moe.ipynb) and [standalone-qw
 
 <img src="https://sebastianraschka.com/images/LLMs-from-scratch-images/bonus/qwen/qwen3-coder-flash-overview.webp?123" width="430px">
 
+&nbsp;
+# Qwen3 from-scratch code
+
+The standalone notebooks in this folder contain from-scratch codes in linear fashion:
+
+1. [standalone-qwen3.ipynb](standalone-qwen3.ipynb): The dense Qwen3 model without bells and whistles
+2. [standalone-qwen3-plus-kvcache.ipynb](standalone-qwen3-plus-kvcache.ipynb): Same as above but with KV cache for better inference efficiency
+3. [standalone-qwen3-moe.ipynb](standalone-qwen3-moe.ipynb): Like the first notebook but the Mixture-of-Experts (MoE) variant
+4. [standalone-qwen3-moe-plus-kvcache.ipynb](standalone-qwen3-moe-plus-kvcache.ipynb): Same as above but with KV cache for better inference efficiency
+
+Alternatively, I also organized the code into a Python package [here](../../pkg/llms_from_scratch/) (including unit tests and CI), which you can run as described below.
+
+&nbsp;
+# Training
+
+The `Qwen3Model` class is implemented in a similar style as the `GPTModel` class, so it can be used as a drop-in replacement for training in chapter 5 and finetuning in chapters 6 and 7.
 
 
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