**We recommend `ThinkingCap-gemma-4-26B-A4B-it-MXFP4_MOE.gguf` over `Q4_K_M` at this size**: it is the file evaluated below, and it held up better on our math benchmarks. Pull it with `-hf bottlecapai/ThinkingCap-gemma-4-26B-A4B-it-GGUF:MXFP4_MOE`. GGUF / llama.cpp quantizations of bottlecapai/ThinkingCap-gemma-4-26B-A4B-it — ThinkingCap's shorter-thinking post-training applied to gemma-4-26B-A4B-it, a mixture-of-experts model with image input. ➡️ Full model description, evaluation results, the measured thinking-token reduction and recommended sampling: see the main model card at bottlecapai/ThinkingCap-gemma-4-26B-A4B-it.
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View details ↗GGUF / llama.cpp quantizations of bottlecapai/ThinkingCap-Qwen3.8-27B — the ThinkingCap finetune of Qwen3.8-27B that cuts reasoning tokens by 37% on average while holding 85.8% average accuracy against the base model's 86.6%. ➡️ Full model description, evaluation results (multi-seed, statistically tested), the measured thinking-token reduction, recommended sampling params, and citation: see the main model card at bottlecapai/ThinkingCap-Qwen3.8-27B. The low-bit files are built with an **importance matrix** (activation statistics from a chat-templated calibration corpus) and a **per-tensor precision layout**: the attention projections of the full-attention layers and the linear-attention output projections stay at 6–8 bit while the feed-forward weights take the 4-bit hit.
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View details ↗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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