Update README.md

README.md

@@ -20,10 +20,12 @@ I used [exllamav3 version 0.0.2](https://github.com/turboderp-org/exllamav3/rele
 ## EXL3 Quantized Models
 
 [4.0bpw](https://huggingface.co/LLMJapan/Qwen2.5-Coder-32B-Instruct_exl3/tree/4.0bpw)
+
 [6.0bpw](https://huggingface.co/LLMJapan/Qwen2.5-Coder-32B-Instruct_exl3/tree/6.0bpw)
+
 [8.0bpw](https://huggingface.co/LLMJapan/Qwen2.5-Coder-32B-Instruct_exl3/tree/8.0bpw)
 
-For coding, I found >6.0bpw or preferably 8.0bpw model with KV Cache Quantization (>6.0bpw) is much better than 4.0bpw.
+For coding, I found >6.0bpw or preferably 8.0bpw model with KV Cache Quantization (>Q6) is much better than 4.0bpw.
 If you are using these models only for short Auto Completion, 4.0bpw is usable.
 
 ## Credits