Qwopus3.6-27B-v2 · ROCmFPX

Stock Q6_K quality, ~25% faster prompt-processing on AMD Strix Halo (gfx1151)

ROCmFPX 3→8-bit quants of Jackrong/Qwopus3.6-27B-v2-MTP-GGUF — the general-purpose 27B Qwopus, MTP speculative decoding + Qwen3-VL vision, agent/tool-use tuned.

⚠️ Requires the ROCmFPX fork (build main — the FP* types are merged in) — custom AMD quant types (enum IDs 110–115), not upstream-stable. Won't load in stock llama.cpp / LM Studio / Ollama. HF's precision badge is wrong — pick the file by name.

Pick a tier

Agent-routed _AGENT tiers + the full enum/bpw table are in the details below and the Files tab. All filenames prefixed Qwopus3.6-27B-v2-MTP-STRIX-.

Quick start

# build the fork once — main already has the ROCmFPX quant types
git clone https://github.com/charlie12345/ROCmFPX.git && cd ROCmFPX
JOBS=16 scripts/build-strix-rocmfp4-mtp.sh

# serve the flagship — MTP + vision
HSA_OVERRIDE_GFX_VERSION=11.5.1 build-strix-rocmfp4/bin/llama-server \
  -m Qwopus3.6-27B-v2-MTP-STRIX-embF16-headQ6-Q6_0_ROCMFPX_AGENT.gguf \
  -dev ROCm0 -ngl 999 -fa on -c 131072 \
  --spec-type draft-mtp --spec-draft-ngl all --spec-draft-n-max 2 \
  --jinja --mmproj mmproj/mmproj-F32.gguf --host 0.0.0.0 --port 8080

Tool calls: point your client at the qwen3_coder parser, or the model narrates instead of emitting structured calls.

All tiers · recipe · benchmarks

All tiers

File suffix	Preset	Enum	Size	Role
embF16-headQ6-Q6_0_ROCMFPX_AGENT.gguf	Q6_0_ROCMFPX_AGENT	114	26 GB	flagship — f16 emb + Q6_K head + imatrix
embF16-Q8_0_ROCMFPX_AGENT.gguf	Q8_0_ROCMFPX_AGENT	115	30 GB	highest-fidelity agent
embF16-Q8_0_ROCMFPX.gguf	Q8_0_ROCMFPX	111	29 GB	highest fidelity
embF16-Q6_0_ROCMFPX.gguf	Q6_0_ROCMFPX	110	24 GB	balanced
embF16-Q3_0_ROCMFPX_AGENT.gguf	Q3_0_ROCMFPX_AGENT	113	21 GB	smallest agent
embF16-Q3_0_ROCMFPX.gguf	Q3_0_ROCMFPX	112	17 GB	smallest
embF16-Q4_0_ROCMFP4.gguf	Q4_0_ROCMFP4	100	19 GB	fastest decode (4-bit body)

f16 token embeddings throughout; _AGENT presets keep attention/FFN routing at higher precision for tool-call/JSON coherence. (HF labels Q4/Q8 but not Q6/Q3 — the latter aren't standard llama.cpp quant names.)

Verification (Strix Halo gfx1151)

Metric	Value
Functional smoke	chat/coding/JSON/tool-call/coherency ✅ (5/5)
PPL vs Q6_K (code corpus)	flagship 2.962 vs Q6_K 2.954 → +0.28% (within ±0.04)

Performance — prompt-processing throughput (t/s) vs Q6_K

Context	Q6_K	flagship	Δ
pp512	195	248	+27%
pp2048	189	242	+28%
pp10k	179	222	+24%
pp16k	172	208	+21%
pp32k	157	186	+19%
pp64k	133	155	+16%

The gfx1151-tuned kernels win the compute-bound prefill; the edge is largest at short context and narrows toward +16% at 64k as O(n²) attention takes over. Decode is bandwidth-bound (≈ Q6_K raw), and MTP (--spec-type draft-mtp) ~doubles it in serving. Q4_0_ROCMFP4 is the decode king (~13 tok/s raw). Single-rep llama-bench, ran warm — absolutes run a touch conservative vs the Coder sibling.

Credits & license

Apache-2.0 (inherited). Jackrong + Kyle Hessling (fine-tune) → Qwen3.6-27B (base) → charlie12345 / ROCmFPX (quant fork). ROCmFPX quantization by this repo's author.