Has anyone experimented with stabilizing low quant models with lower temp and to

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1s59rct/rys_part_3_llms_think_in_geometry_not_language/"> <img alt="RYS Part 3: LLMs think in geometry, not

<!-- SC_OFF --><div class="md"><p>I have experience using llama.cpp on Windows/Linux with 8GB NVIDIA card (384 GB/s bandwidth) and offloading to CPU to run MoE models. I typically

<!-- SC_OFF --><div class="md"><p>Currently using glm 4.7 flash, it’s very meh </p> <p>Heard omnicoder or Crow 9b are good, are they any better?</p> <p>Or Qwen3.5 27b?</p>

<!-- SC_OFF --><div class="md"><p>&#x200b;</p> <p>## Abstract</p> <p>Post-training makes language models more decisive without necessarily making them more accurate — and we find

<!-- SC_OFF --><div class="md"><p>Long time Claude Opus user, but after the recent session limit changes by Anthropic, I am seriously considering trying Chinese models for coding.

<!-- SC_OFF --><div class="md"><p>I’ve been working on an educational implementation repo for speculative decoding:</p> <p><a

<!-- SC_OFF --><div class="md"><p>Just read the google recent blog post they're claiming 6x KV cache compression with zero accuracy loss and up to 8x attention speedup on H100s.

<!-- SC_OFF --><div class="md"><p>is it possible that a moe model can decide how many billion parameters to activate per token according to the task. eg if qwen 3.6 35b a3b - if a