A 27B dense vision-language model (27.8B including the vision tower). 64 decoder layers in a hybrid stack: `full_attention_interval=4` means every fourth layer is grouped-query full attention (16 layers) and the rest are Gated DeltaNet linear attention (48 layers). Vocab 248320 with untied embeddings. Context length 262144 natively, extensible further with YaRN. The mmproj file is separate by design: llama.cpp loads the text backbone and the vision projector as two files. Download one tier plus one mmproj. Without the mmproj the model still runs, but text-only. The `mmproj-` and `mtp-` prefixes are what recent llama.cpp builds match on to tell the three file kinds apart, so `-hf 6block/Qwen3.8-27B-GGUF` picks a backbone rather than a projector, and `--spec-type mtp` finds the draft on its own.
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View details ↗Zero-requantization expert-sliced GGUF checkpoints of `ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF` (`IQ3_S` / `Q2_0` expert tensors preserved bit-identically), calibrated over **131,072 multi-domain tokens** (`1,310,720` top-10 router picks per layer across 48 layers; 35% ops/bash/Terminal-Bench, 30% chat/JSON/reasoning, 35% Japanese).
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