---

license: gemma

library_name: gguf

base_model: coder3101/gemma-4-26B-A4B-it-heretic

base_model_relation: quantized

model_name: Gemma-4-26B-A4B-it-Heretic-Cerebellum-v1.1-templatefix-GGUF

model_type: gemma4

quantized_by: deucebucket

pipeline_tag: text-generation

tags:

- GGUF

- gemma4

- gemma

- quantized

- cerebellum

- imatrix

- moe

- 3-bit

- templatefix

model-index:

• name: Gemma-4-26B-A4B-it-Heretic-Cerebellum-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: 0.9548

source:

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

url: https://huggingface.co/deucebucket/Gemma-4-26B-A4B-it-Heretic-Cerebellum-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: 0.8349

source:

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

url: https://huggingface.co/deucebucket/Gemma-4-26B-A4B-it-Heretic-Cerebellum-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: 0.7142

source:

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

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

- task:

name: Text Generation

type: text-generation

dataset:

name: HumanEval+ (pass@1)

type: openai_humaneval

split: test

metrics:

- name: pass@1

type: pass@1

value: 0.8963

source:

name: Local audited benchmark run (RTX 3090, llama.cpp), chat no-think harness, patched evalplus

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

---

<p align="center">

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

</p>

Gemma 4 26B-A4B-it Heretic Cerebellum GGUF

Sensitivity-guided mixed-precision GGUF of coder3101/gemma-4-26B-A4B-it-heretic,

a decensored variant of google/gemma-4-26B-A4B-it.

It uses the Cerebellum v6 tensor allocation transferred verbatim onto the heretic

weights. The shipped file carries Google's updated Gemma 4 chat-template metadata

(2026-05-18 state) with zero tensor changes. Versions appear in filenames, not the repo name.

Files

| File | Description |

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

| gemma-4-26B-A4B-it-heretic-cerebellum-v1.1-templatefix-Q3_K_M.gguf | ~11 GB; v1 allocation + updated chat-template metadata |

| gemma-4-26B-A4B-it-heretic.mmproj-f16.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. The v1.1 templatefix file is metadata-only

over v1, so these describe the same weights. The comparison column is our own plain

(non-heretic) Cerebellum v6 build on the same harness, shown so the abliteration cost

is visible. Summary JSONs and per-question samples are in benchmark_results/.

| Benchmark | Heretic v6 alloc (11 GB) | Plain Cerebellum v6 (11 GB) |

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

| ARC-Challenge (1172 q) | 95.48% | 95.56% |

| HellaSwag (10042 q) | 83.49% | 84.55% |

| MMLU-Redux (2400 q) | 71.42% | 71.33% |

| HumanEval base (chat, no-think) | 92.07% | pending re-audit |

| HumanEval+ (chat, no-think) | 89.63% | pending re-audit |

| Vision smoke | 6/6 | — |

HumanEval/HumanEval+ used the chat-completions harness

(scripts/benchmark_evalplus_chat.py, enable_thinking: false,

thinking_budget_tokens: 0, BENCH_WORKERS=1, max_tokens: 768). The completion

audit for that run recorded 0 prompt echoes, 0 repeated function definitions,

0 pass-only outputs, and 2 genuine syntax failures. The plain v6 HumanEval artifacts

were raw-completions and are marked for re-audit, so no plain-v6 HumanEval is published.

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-heretic-cerebellum-v1.1-templatefix-Q3_K_M.gguf \
  --mmproj gemma-4-26B-A4B-it-heretic.mmproj-f16.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: context to 131,072. This rig's

measurements; no quality claims beyond them.

Provenance

• Source (heretic): coder3101/gemma-4-26B-A4B-it-heretic — abliterated variant
• Original family: google/gemma-4-26B-A4B-it — Google Gemma Team
• Recipe: Cerebellum v6 tensor allocation transferred to the matching heretic layout;

v1.1 is a chat-template metadata refresh (Google 2026-05-18 template), zero tensor changes

Credits

• Source model: coder3101/gemma-4-26B-A4B-it-heretic
• Original Gemma family: Google Gemma Team
• GGUF runtime: llama.cpp
• Quantization method: Cerebellum — deucebucket