Update README.md

README.md

@@ -23,6 +23,20 @@ The model weights included here are PyTorch state dicts converted from the offic
 
 To avoid duplication and ease maintance, this repository only contains the model weights; the self-contained source code can be found [here](https://github.com/rasbt/LLMs-from-scratch/blob/main/pkg/llms_from_scratch/qwen3.py). Instructions on how to use the code are provided below.
 
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+# Qwen3 from-scratch code
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+The standalone notebooks in this folder contain from-scratch codes in linear fashion:
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+1. [standalone-qwen3.ipynb](https://github.com/rasbt/LLMs-from-scratch/blob/main/ch05/11_qwen3/standalone-qwen3.ipynb): The dense Qwen3 model without bells and whistles
+2. [standalone-qwen3-plus-kvcache.ipynb](https://github.com/rasbt/LLMs-from-scratch/blob/main/ch05/11_qwen3/standalone-qwen3-plus-kvcache.ipynb): Same as above but with KV cache for better inference efficiency
+3. [standalone-qwen3-moe.ipynb](https://github.com/rasbt/LLMs-from-scratch/blob/main/ch05/11_qwen3/standalone-qwen3-moe.ipynb): Like the first notebook but the Mixture-of-Experts (MoE) variant
+4. [standalone-qwen3-moe-plus-kvcache.ipynb](https://github.com/rasbt/LLMs-from-scratch/blob/main/ch05/11_qwen3/standalone-qwen3-moe-plus-kvcache.ipynb): Same as above but with KV cache for better inference efficiency
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+Alternatively, I also organized the code into a Python package (including unit tests and CI), which you can run as described below.
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+
  
 
 ### Using Qwen3 0.6B via the `llms-from-scratch` package