---

license: gemma

library_name: gguf

base_model: google/gemma-4-26B-A4B-it

base_model_relation: quantized

model_name: Gemma-4-26B-A4B-it-Cerebellum-v6.1-templatefix-GGUF

model_creator: google

model_type: gemma4

quantized_by: deucebucket

pipeline_tag: text-generation

tags:

- GGUF

- gemma4

- gemma

- google

- quantized

- cerebellum

- imatrix

- moe

- 3-bit

- templatefix

model-index:

• name: Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF

results:

- task:

name: Text Generation

type: text-generation

dataset:

name: AI2 Reasoning Challenge

type: ai2_arc

config: ARC-Challenge

split: test

metrics:

- name: normalized accuracy

type: acc_norm

value: 0.9556

source:

name: Local audited benchmark run (RTX 3090, llama.cpp)

url: https://huggingface.co/deucebucket/Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF/tree/main/benchmark_results

- task:

name: Text Generation

type: text-generation

dataset:

name: HellaSwag

type: hellaswag

split: validation

metrics:

- name: accuracy

type: acc

value: 0.8455

source:

name: Local audited benchmark run (RTX 3090, llama.cpp)

url: https://huggingface.co/deucebucket/Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF/tree/main/benchmark_results

- task:

name: Text Generation

type: text-generation

dataset:

name: MMLU-Redux

type: cais/mmlu

config: all

split: test

metrics:

- name: accuracy

type: acc

value: 0.7133

source:

name: Local audited benchmark run (RTX 3090, llama.cpp)

url: https://huggingface.co/deucebucket/Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF/tree/main/benchmark_results

---

<p align="center">

<img src="cerebellum_banner.png" alt="Cerebellum" width="640">

</p>

Gemma 4 26B-A4B-it Cerebellum GGUF

Sensitivity-guided mixed-precision GGUF of google/gemma-4-26B-A4B-it:

a Q3_K_M base with the Cerebellum v6 tensor allocation. The shipped file carries

the v6 weights plus Google's updated Gemma 4 chat-template metadata (the 2026-05-18

template state) with zero tensor changes versus v6. Newer versions appear in

filenames, not the repo name.

Files

| File | Description |

|------|-------------|

| gemma-4-26B-A4B-it-cerebellum-v6.1-templatefix-Q3_K_M.gguf | ~11 GB; v6 allocation + updated chat-template metadata |

| gemma-4-26b-a4b-it.mmproj.gguf | vision projector (required for image/video) |

Evaluation

Measured directly on the GGUF with llama.cpp llama-server on an RTX 3090,

temperature 0, project benchmark harness. v6.1 is metadata-only over v6, so these

describe the same weights. The comparison column is our own same-size uniform

Q3_K_M build measured on the same harness. Summary JSONs are in benchmark_results/.

| Benchmark | Cerebellum v6 (11 GB) | Uniform Q3_K_M (11 GB) |

|-----------|:---:|:---:|

| ARC-Challenge (1172 q) | 95.56% | 95.22% |

| HellaSwag (10042 q) | 84.55% | 86.57% |

| MMLU-Redux (2400 q) | 71.33% | 73.67% |

| HumanEval (raw-completions, legacy) | pending re-audit | 62.2% pass@1 |

HumanEval for Gemma 4 must use the chat-completions harness

(scripts/benchmark_evalplus_chat.py, enable_thinking: false,

thinking_budget_tokens: 0, BENCH_WORKERS=1). The retained v6 HumanEval

artifacts were raw-completions and are marked for re-audit, so no v6 HumanEval

number is published here.

Usage

Gemma 4 requires --jinja. For non-thinking output, pass request-level

chat_template_kwargs: {"enable_thinking": false} and thinking_budget_tokens: 0;

do not set a fixed server --reasoning-budget (it can burn output into hidden

reasoning until the length cap, which looks like a repetition loop).

llama-server \
  --model gemma-4-26B-A4B-it-cerebellum-v6.1-templatefix-Q3_K_M.gguf \
  --mmproj gemma-4-26b-a4b-it.mmproj.gguf \
  -ngl 99 --ctx-size 65536 --parallel 1 --flash-attn on \
  --cache-type-k q8_0 --cache-type-v q8_0 --jinja --reasoning auto

Measured on one RTX 3090 (24 GB), KV q8_0: ~123 tok/s decode, 15.1 GB peak VRAM

(4-slot serving), context to 131,072. This rig's measurements; no quality claims

beyond them.

Provenance

• Base: google/gemma-4-26B-A4B-it — Google Gemma Team
• Base quant lineage: Q3_K_M with the bartowski imatrix (bartowski/google_gemma-4-26B-A4B-it-GGUF)
• Recipe: Cerebellum v6 tensor allocation; v6.1 is a chat-template metadata refresh

(Google 2026-05-18 template), zero tensor changes

Credits

• Base model: Google Gemma Team, google/gemma-4-26B-A4B-it
• Imatrix: bartowski, bartowski/google_gemma-4-26B-A4B-it-GGUF
• GGUF runtime: llama.cpp
• Quantization method: Cerebellum — deucebucket