Sam Witteveen · 2026-07-30 · notable
Sam Witteveen — 'ThinkingCap: The Local Coding Model'
Sam Witteveen walks through ThinkingCap-Qwen3.6-27B, BottleCap AI's Apache-2.0 fine-tune of Qwen3.6-27B that keeps benchmark scores while cutting reasoning tokens by about 46%, aimed at local coding on a single 24 GB GPU.
Sam Witteveen benchmarks ThinkingCap-Qwen3.6-27B for local coding — same accuracy, roughly half the thinking tokens.
What is it?
Sam Witteveen's new video reviews ThinkingCap-Qwen3.6-27B, an Apache-2.0 fine-tune of Alibaba's Qwen3.6-27B released by BottleCap AI on July 13. The model targets developers running a local coding assistant on a single 24 GB GPU and is published on Hugging Face for direct download.
How does it work?
ThinkingCap trains the base Qwen3.6-27B checkpoint with a reward that credits shorter, sufficient reasoning traces instead of only correctness. The result is roughly 46% fewer reasoning tokens on average across evaluation domains, with LiveCodeBench going up 3.6 points to 84.3% and GPQA-Diamond staying within 1.6 points of the base at 83.8%.
Why does it matter?
Local coding assistants are gated on tokens-per-second and VRAM budget; cutting thinking tokens in half at the same accuracy makes ThinkingCap a drop-in replacement for a Qwen3.6-27B loop running on a single RTX 4090 or 3090. Sam Witteveen's channel is where a lot of local-LLM builders decide what to try next.
Who is it for?
local-LLM builders, indie coding-agent developers
Try it
Watch the video, then pull `bottlecapai/ThinkingCap-Qwen3.6-27B` from Hugging Face