Update README.md

README.md

@@ -21,6 +21,7 @@ First and foremost, I wanted to thank everyone over on the KoboldAI discord serv
 - Henky as well, for also loaning me some of their runpod hours, and helping me troubleshoot some issues with getting KCPP to work with LocalAIME
 - Everyone else on the KoboldAI discord server, there were more than a few willing to help me out in the way of advice, troubleshooting, or offering me their machines or runpod hours to help with testing if the above didn't get to it first. 
 - EntropyMagnets on reddit for making and sharing his LocalAIME tool
+- Big thanks to https://huggingface.co/none-user for running these tests at a higher precision https://huggingface.co/lemon07r/Qwen3-R1-SLERP-Q3T-8B/discussions/2 
 
 I would also like to thank Mradermacher and Bartowski for always posting quants of the models I upload, and the very many other models they get to as well. 
 
@@ -51,6 +52,8 @@ This data is available down below in the results github repo, along with my resu
 Due to limitations of available machine power, and the large amount of context used (30k context was used for all testing) I was only able to test these models with Q4_K_S static quants, and only 1 attempt at each problem, and it still took very long to get it all done.
 It would have been better if I could have tested at higher precision (at least Q8_0), and with more attempts per problem (at least 3-5). If anyone with the means is willing to run their own tests under those better circumstances I hope they share their findings with the community, and if anyone with GPU power wants to sponsor my efforts and let me rerun these tests under better coniditions I would be more than happy to, just reach out to me here or on discord (mim7).
 
+EDIT - @none_user has done exactly this, testing at FP16 and on 3 attempts vs my 1 attempt per problem. Both SLERP merges tested very well, both performing much better than their parents, with the Q3T merge using the Qwen tokenizer being the best of the bunch overall. These slerp merges turned out much better than I expected. Hopefully people will start using this as the base for their future finetunes. 
+
 ### The Other Model
 
 This DST merge uses the Deepseek tokenizer (and for now, until further testing seems to be the not quite as good, and use more tokens to think).
@@ -68,6 +71,9 @@ https://github.com/jabberjabberjabber/LocalAIME_Kobo
 
 ### LocalAIME Results
 
+EDIT - @none_user has run the same test at a higher precision with more problem attempts here https://huggingface.co/lemon07r/Qwen3-R1-SLERP-Q3T-8B/discussions/2
+This is much higher quality data and testing. Big thanks to him for doing this. 
+
 ![Performance vs tokens generated](https://github.com/lemon07r/LocalAIME_results/blob/main/plots/accuracy_vs_tokens.png?raw=true)
 
 ![performance](https://github.com/lemon07r/LocalAIME_results/blob/main/plots/accuracy_comparison.png?raw=true)