Everything needed to run HauhauCS/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive with DFlash speculative decoding in llama.cpp: all target quants, the mmproj (vision), and the DFlash draft head in five quant levels.

DFlash is lossless — output is byte-identical to running the target alone; the draft only makes generation faster (measured +9–17% over the MTP head, see below). The target and draft are always two separate files loaded together; any target quant pairs with any draft quant.

Target model quants (choose one)

File	Size
...-IQ2_M.gguf	10.0 GB
...-Q2_K_P.gguf	11.5 GB
...-IQ3_XS.gguf	12.0 GB
...-IQ3_M.gguf	12.6 GB
...-Q3_K_P.gguf	14.3 GB
...-IQ4_XS.gguf	15.1 GB
...-Q4_K_P.gguf	17.5 GB
...-Q5_K_P.gguf	20.8 GB
...-Q6_K_P.gguf	23.2 GB
...-Q8_K_P.gguf	32.0 GB

Plus mmproj-...-f16.gguf (0.93 GB) for vision input.

DFlash draft head quants (choose one)

File	Size	Notes
Qwen3.6-27B-DFlash-BF16.gguf	3.5 GB	Best acceptance — recommended
Qwen3.6-27B-DFlash-Q8_0.gguf	1.9 GB	Near-lossless
Qwen3.6-27B-DFlash-Q6_K.gguf	1.4 GB
Qwen3.6-27B-DFlash-Q5_K.gguf	1.2 GB
Qwen3.6-27B-DFlash-Q4_K_M.gguf	1.0 GB	Smallest

Pairing: any head works with any target

The draft head and target quant are independent choices — llama.cpp loads them as two separate models, so every head in this repo pairs with every target quant above. There is no need for a Q2/Q3/IQ head to match a Q2/Q3/IQ target. Suggested pairing: BF16 head if you have ~3.5 GB VRAM to spare (best acceptance), Q4_K_M head (1 GB) if squeezed. Avoid quantizing the head harder than that: draft quality sets your speedup (we measured code acceptance drop 69% -> 56% just going BF16 -> Q8_0), so a Q2-class head would erase the benefit of speculation.

Usage (llama.cpp with DFlash support, arch `dflash`)

bash

llama-server \
  -m Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-Q8_K_P.gguf \
  --mmproj mmproj-Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-f16.gguf \
  -ngl 99 -fa on \
  --spec-type draft-dflash \
  --spec-draft-model Qwen3.6-27B-DFlash-BF16.gguf \
  --spec-draft-n-max 6 \
  --spec-draft-ngl 99

Measured results

Q8_K_P target + BF16 draft, RTX PRO 6000 Blackwell 96 GB, 200-token greedy completions. Baseline: same target with the Qwen3.6-27B MTP head (--spec-type draft-mtp, n-max 5).

Workload	MTP baseline	DFlash	Speedup	DFlash acceptance (mean run len)
Code	108.5 tok/s	126.6 tok/s	+16.6%	69.3% (5.10)
Math	146.0 tok/s	159.9 tok/s	+9.5%	85.6% (6.03)
Chat	88.4 tok/s	96.7 tok/s	+9.5%	43.9% (3.60)

Q8_0 draft on code: 98 tok/s at 55.6% acceptance — quantizing the draft trades acceptance for VRAM; prefer BF16 unless memory-constrained. The drafter was trained against stock Qwen3.6-27B yet acceptance holds up well on this finetune; output verified identical with and without speculation.

Attribution

Target finetune + quants: HauhauCS/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive
Drafter weights: z-lab/Qwen3.6-27B-DFlash (MIT)
DFlash llama.cpp support: ggml-org/llama.cpp
Draft quantized with llama-quantize from BF16

zeeksa/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-DFlash-GGUF

Qwen3.6-27B Uncensored HauhauCS Aggressive + DFlash — complete GGUF bundle