will definitively try it. Overall with a smarter model the improvement is in the token usage and latency. Tbh I think i could get a 10 score if i tuned the prompt and temperature a little more. The original author harness is already giving 9.6 score which is pretty good. However this harness loop can take more than 1 hour on the hardest problem at 100 token /sec. A smarter model such as 3.8-flash-next could get 10 score and resolve the hardest problem faster. That's my estimate i'll give it a try. Thx

Bonsai 2-bit quantization is notably dumber than Qwen 27B at eight bits or even four bits. I doubt you'll get this level of quality even with five of them.

Bonsai 2 has a bit different architecture as far I understand and speculative decoding is not so great yet. Before that point, original model is faster if you have the memory. Would be nice to see the speed with proper speculative decoder.

Here the intent is to replicate GVS5H with memdoor a headless coding agent I created months ago. Anyone can reshape the harness by redefining the steps in yaml files , models and temperatures. Up to you guys to try you have the recipe in this repo.