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posted an update about 7 hours ago
🚀 Gemma-4-A4B 98e v6-coder (C6v3lcb) — LCB-targeted code prune of Gemma 4 26B-A4B, 20.8B MoE (4B-active). Same C6 recipe as v5-coder, re-steered specifically at LiveCodeBench-medium — the one code bench pruning hurt most.
Not only keeps the lead on Python and closes the gap to 1-2pp in the other coding languages.
It's actually reasoning better, fixing the under-thinking and over-thinking failures of the full experts router.
All this comes with a cost with only 20b, on top of being very specific to coding; about 3x the thinking tokens in LiveCodeBench but it's good thinking that brings home not only more correct answers but in general a more precise and concise output.
📊 SCORES (Q6_K, llama.cpp, greedy, EVAL_PROTOCOL v3)
HumanEval 98.78 — HumanEval+ 93.29 — LCB-medium-55 v4 96.36
LCB-medium-100 96.00 — MultiPL-E macro 88.00 (Rust/Java/JS)
MATH-500 91.00 — GPQA-D 67.17 — AIME 63.33 — IFEval 92.00
vs v5-coder: +10.91 LCB-medium / +7.0 MultiPL-E / +10 AIME, HE+ tie
LCB targeting closed the −9.10pp hole and pushed +1.81pp past the unpruned 128e. Top of the 14–22B coder band: +9.2pp HE over Qwen2.5-Coder-14B-Instruct (89.6 → 98.78).
📦 GGUF SWEEP (all imatrix; Q4_K_M plain — imatrix hurt it)
Q6_K — 17.81 GB — 93.29% (cohort top)
Q3_K_M — 10.51 GB — 92.68% ⭐ value leader (imatrix lifted the 3-bit tiers hard)
IQ4_XS — 11.01 GB — 92.07% ⭐ safe 4-bit
IQ3_XS — 9.22 GB — 92.07% — smallest on the plateau
IQ2_S — 7.83 GB — 89.02% — sub-8 GB code-grade
⚔️ SAME-RIG vs Qwen2.5-Coder-14B (RTX 3090, greedy)
Iso-disk 10.5 GB: Q3_K_M 92.68 vs Qwen Q5_K_M 83.54 → +9.14pp at the same file size
LCB-medium-55 v4, identical split: 96.36 vs 18.18
bf16:
ManniX-ITA/gemma-4-A4B-98e-v6-coder-it
GGUF:
ManniX-ITA/gemma-4-A4B-98e-v6-coder-it-GGUF
Ollama:
https://ollama.com/mannix/gemma4-98e-v6-coder updated a model about 18 hours ago
ManniX-ITA/Qwen3.5-4B-MicroCoder-GGUF updated a model about 18 hours ago
ManniX-ITA/Qwen3.5-4B-MicroCoderOrganizations
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