Update README.md

README.md

@@ -12,7 +12,7 @@ The official QAT weights released by google use fp16 (instead of Q6_K) for the e
 ~~Instead of quantizing the table myself, I extracted it from Bartowski's quantized models, because those were already calibrated with imatrix, which should squeeze some extra performance out of it.~~ 
 Requantizing with llama.cpp fixes that and gives better result than the other thing.
 
-Here are some benchmar results:
+Here are some benchmark results:
 
 | Model | File size ↓ | PPL (wiki.text.raw) ↓ | Hellaswag (first 4000 tasks, deterministic) ↑ |
 | --- | --- | --- | --- |
@@ -26,4 +26,8 @@ Here are some benchmar results:
 Note that this model ends up smaller than the Q4_0 from Bartowski. This is because llama.cpp sets some tensors to Q4_1 when quantizing models to Q4_0 with imatrix, but this is a static quant.
 The perplexity scores are within margin of error between this model and the original QAT, despite the size difference. 
 
-The drop in Hellaswag score with the older version of the model is what made me realize there was probably something missing with my previous approach. It's much better now.
+The drop in Hellaswag score with the older version of the model is what made me realize there was probably something missing with my previous approach. It's much better now.
+
+I also fixed the control token metadata, which was slightly degrading the performance of the model in instruct mode. Shoutout to ngxson for [finding the issue](https://huggingface.co/google/gemma-3-12b-it-qat-q4_0-gguf/discussions/3#67f6a2e0207b4bceea793151),
+tdh111 for [making me aware of the issue](https://huggingface.co/stduhpf/google-gemma-3-27b-it-qat-q4_0-gguf-small/discussions/3#67f74fdf8411d4d6a82049db), 
+and u/dampflokfreund on reddit ([Dampfinchen](https://huggingface.co/Dampfinchen) on Huggingface) for [sharing the steps to fix it](https://www.reddit.com/r/LocalLLaMA/comments/1jvi860/comment/mmcuvw2).