Post
137
🚀 Gemma-4-A4B 98e v5-coder — code-leaning 20.8B MoE (4B-active), C6 layer-relevance-weighted prune of Gemma 4 26B-A4B. Best 20B-class coder I've shipped.
📊 SCORES (NVFP4A16, vLLM 0.20.2, greedy, EVAL_PROTOCOL v3)
HumanEval 98.17 — HumanEval+ 92.68 — LCB-medium-55 v4 85.45
MATH-500 92.00 — GPQA-D 68.69 — IFEval 94.00
vs v4: +1.22 HE / +1.22 HE+ / +7.27 LCB-medium
Top of the 14–22B coder band: +8.6pp HE over Qwen2.5-Coder-14B-Instruct (89.6 → 98.17). HE+ sanity-audited — no memorization, no silent-empty.
📦 EXTENSIVE GGUF SWEEP (16 plain + IQ tiers + 5 CD recipes, all imatrix-calibrated)
Q8_0 — 21.16 GB — 93.90% (cohort top)
Q4_K_S — 12.21 GB — 93.29% ⭐ plain sweet spot
IQ4_XS — 11.01 GB — 93.29% ⭐ sub-12 GB top
⭐ TWO EXCELLENT SUB-10 GB CONTRIBDYNAMIC CD PICKS (per-layer + IQ-codebook overrides)
CD-IQ4_K_M (Canary W) — 10.29 GB — 92.07% — recommended sub-11 GB
CD-IQ3_XS_L — 9.27 GB — 90.24% — smallest viable code-grade
⚔️ SAME-RIG vs Qwen2.5-Coder-14B-Instruct (RTX 3090, greedy HE+)
11 GB band: v5-coder IQ4_XS wins +9.75pp at -1.49 bpw
12 GB band: Q4_K_S wins +8.53pp
8 GB band: IQ2_S wins +0.61pp at lower bpw
bf16:
ManniX-ITA/gemma-4-A4B-98e-v5-coder-it
GGUF:
ManniX-ITA/gemma-4-A4B-98e-v5-coder-it-GGUF
NVFP4A16:
ManniX-ITA/gemma-4-A4B-98e-v5-coder-NVFP4A16
Ollama:
https://ollama.com/mannix/gemma4-98e-v5-coder
———
🆕 BONUS — Qwen3.6-27B-Omnimerge-v4-MTP-GGUF
Same v4 weights with the native MTP head retained for llama.cpp speculative decoding (PR #22673, --spec-type draft-mtp). 7 imatrix tiers Q8_0 → IQ2_M.
HumanEval: 2.0x decode tok/s
MBPP: 2.33x decode tok/s
Both at +1-2pp pass@1 vs the non-MTP build. GPQA Diamond comparison in flight.
MTP-GGUF:
ManniX-ITA/Qwen3.6-27B-Omnimerge-v4-MTP-GGUF
📊 SCORES (NVFP4A16, vLLM 0.20.2, greedy, EVAL_PROTOCOL v3)
HumanEval 98.17 — HumanEval+ 92.68 — LCB-medium-55 v4 85.45
MATH-500 92.00 — GPQA-D 68.69 — IFEval 94.00
vs v4: +1.22 HE / +1.22 HE+ / +7.27 LCB-medium
Top of the 14–22B coder band: +8.6pp HE over Qwen2.5-Coder-14B-Instruct (89.6 → 98.17). HE+ sanity-audited — no memorization, no silent-empty.
📦 EXTENSIVE GGUF SWEEP (16 plain + IQ tiers + 5 CD recipes, all imatrix-calibrated)
Q8_0 — 21.16 GB — 93.90% (cohort top)
Q4_K_S — 12.21 GB — 93.29% ⭐ plain sweet spot
IQ4_XS — 11.01 GB — 93.29% ⭐ sub-12 GB top
⭐ TWO EXCELLENT SUB-10 GB CONTRIBDYNAMIC CD PICKS (per-layer + IQ-codebook overrides)
CD-IQ4_K_M (Canary W) — 10.29 GB — 92.07% — recommended sub-11 GB
CD-IQ3_XS_L — 9.27 GB — 90.24% — smallest viable code-grade
⚔️ SAME-RIG vs Qwen2.5-Coder-14B-Instruct (RTX 3090, greedy HE+)
11 GB band: v5-coder IQ4_XS wins +9.75pp at -1.49 bpw
12 GB band: Q4_K_S wins +8.53pp
8 GB band: IQ2_S wins +0.61pp at lower bpw
bf16:
ManniX-ITA/gemma-4-A4B-98e-v5-coder-it
GGUF:
ManniX-ITA/gemma-4-A4B-98e-v5-coder-it-GGUF
NVFP4A16:
ManniX-ITA/gemma-4-A4B-98e-v5-coder-NVFP4A16
Ollama:
https://ollama.com/mannix/gemma4-98e-v5-coder
———
🆕 BONUS — Qwen3.6-27B-Omnimerge-v4-MTP-GGUF
Same v4 weights with the native MTP head retained for llama.cpp speculative decoding (PR #22673, --spec-type draft-mtp). 7 imatrix tiers Q8_0 → IQ2_M.
HumanEval: 2.0x decode tok/s
MBPP: 2.33x decode tok/s
Both at +1-2pp pass@1 vs the non-MTP build. GPQA Diamond comparison in flight.
MTP-GGUF:
ManniX-ITA/Qwen3.6-27B-Omnimerge-v4-MTP-GGUF