192 5 hours ago

Gemma-4 98e coder max variant, top notch coding skills at the expense of science knowledge

vision tools thinking
ollama run mannix/gemma4-98e-v7-coderx:Q3_K_L

Details

2 days ago

b0a1638c82ab · 11GB ·

gemma4
·
19.9B
·
Q3_K_L
<start_of_turn>{{- if or (eq .Role "system") (eq .Role "user") }}user {{- if (eq .Role "system") }}

Readme

Gemma 4 26B-A4B 98e v7-coderx — code-maximal prune

20.8B params · 98 experts (30 dropped) · ~4B active · code-maximal drop map

A research checkpoint that takes Gemma-4-26B-A4B-it and drops 30128 experts per layer using a code-maximal recipe on the rebuilt v7 competence maps (audited producers, 10 classes) — generic-code 3× + LiveCodeBench-medium 2× on a [24,40] per-layer floor, with no science or multilingual targeting. Same router, attention, and norms as base, plus the mandatory shared-FFN α=1.2 upweight every coder variant carries.

The strongest coder in the cohort: it spends its whole prune budget on code and lands LiveCodeBench-medium-55 at 98.18% and LCB-100 at 99.0% — the highest of any Gemma-4 prune to date, +1.8pp / +2.0pp past the unpruned 128e (96.36 / 97.0). The trade is graduate science (GPQA 48.48). If you need the science back without giving up the code profile, use the sibling v7-coder (GPQA 70.71, LCB-55 96.36).

Full model card & methodology: ManniX-ITA/gemma-4-A4B-98e-v7-coderx-it on Hugging Face.

Other formats: - GGUF (29 tiers, imatrix, CD-* per-layer mixes + F16 + mmproj): ManniX-ITA/gemma-4-A4B-98e-v7-coderx-it-GGUF - NVFP4A16 (native vLLM, ~13 GB): ManniX-ITA/gemma-4-A4B-98e-v7-coderx-NVFP4A16

Scores (Q6_K, llama.cpp, greedy, same host)

LCB-55LCB-100MultiPL-EHEHE+IFEvalGSM8KMATH-500AIMEARCGPQA-D
98.1899.0090.0095.7392.6895.0091.0089.0070.0094.2848.48

Reference columns on the same Q6_K run: unpruned 128e LCB-55 96.36 / LCB-100 97.00 / MultiPL-E 90.00; v6-coder LCB-55 92.73 / LCB-100 94.00. v7-coderx tops the cohort on every code/instruction axis; the budget is paid almost entirely on graduate science (GPQA 48.48, vs 128e 67.17).

GGUF tiers (size guide)

Per-tier HE+ was not swept for the v7 cohort — the Q6_K table above is the reference; quality below ~Q3 / 3-bit degrades on the Gemma 4 MoE, so prefer Q4_K_M or higher for production. Full 29-tier list (incl. CD-* per-layer mixes) is on the GGUF repo.

TierSize (GB)bpwRole
Q8_021.168.14near-lossless
Q6_K17.816.84max fidelity (bench tier)
Q5_K_M15.075.79high quality
Q4_K_M13.245.09:latest — recommended default
Q4_K_S12.214.69compact 4-bit
IQ4_XS11.014.23safe sub-12 GB 4-bit
Q3_K_M10.514.04smallest comfortable
IQ3_XS9.223.54sub-10 GB
IQ2_M8.223.16sub-8.5 GB (degraded)
IQ2_S7.833.01smallest viable

Pull

ollama pull mannix/gemma4-98e-v7-coderx            # :latest = Q4_K_M
ollama pull mannix/gemma4-98e-v7-coderx:IQ4_XS     # safe 4-bit, sub-12 GB
ollama pull mannix/gemma4-98e-v7-coderx:Q6_K       # max fidelity (bench tier)
ollama pull mannix/gemma4-98e-v7-coderx:IQ2_S      # sub-8 GB
ollama pull mannix/gemma4-98e-v7-coderx:vision-Q4_K_M   # + SigLIP vision tower

Inherits Gemma 4’s thinking format — serve with the reasoning parser enabled (--reasoning-format deepseek --reasoning-budget 8192 on llama-server).

Derivative of Gemma 4 — Gemma Terms of Use.