ThinkingCap-Qwen3.8-27B-GGUF-RCO-4.2bpw

A **14.3 GB** GGUF of bottlecapai/ThinkingCap-Qwen3.8-27B that runs on an **18 GB GPU with image input, MTP speculative decoding and a 32k-token context all on the card**, and matched the bf16 weights on all five benchmarks we evaluated (2,863 questions: 81.00% against 81.03%). The standard GGUFs (ThinkingCap-Qwen3.8-27B-GGUF) start at IQ4_XS, 15.5 GB, which with the vision projector and a 32k context does not fit 18 GB. Every weight tensor was quantized with llama.cpp's own types (IQ2_S up to Q8_0, all with one importance matrix computed on the model's own reasoning traces). Which type each of the 401 language-model weight matrices gets was then picked by an RCO search: it minimizes the KL divergence between the bf16 model's and the mixed model's next-token distributions on 256 of the model's own traces, under an exact file-size budget. The same idea as ISTA-DASLab's GSQ-RCO GGUFs, searched on this finetune rather than the base model; the rounding itself is llama.cpp's.

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