Instructions to use deucebucket/Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use deucebucket/Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf deucebucket/Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF:Q3_K_M
# Run inference directly in the terminal:
llama cli -hf deucebucket/Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF:Q3_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf deucebucket/Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF:Q3_K_M
# Run inference directly in the terminal:
llama cli -hf deucebucket/Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF:Q3_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf deucebucket/Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF:Q3_K_M
# Run inference directly in the terminal:
./llama-cli -hf deucebucket/Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF:Q3_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf deucebucket/Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF:Q3_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf deucebucket/Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF:Q3_K_M

Use Docker

docker model run hf.co/deucebucket/Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF:Q3_K_M

LM Studio
Jan

vLLM

How to use deucebucket/Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "deucebucket/Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "deucebucket/Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/deucebucket/Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF:Q3_K_M

Ollama
How to use deucebucket/Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF with Ollama:
```
ollama run hf.co/deucebucket/Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF:Q3_K_M
```
Unsloth Desktop

How to use deucebucket/Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf deucebucket/Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF:Q3_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "deucebucket/Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF:Q3_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use deucebucket/Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF with Docker Model Runner:
```
docker model run hf.co/deucebucket/Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF:Q3_K_M
```

Lemonade

How to use deucebucket/Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull deucebucket/Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF:Q3_K_M

Run and chat with the model

lemonade run user.Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF-Q3_K_M

List all available models

lemonade list

Hermes Agent

How to use deucebucket/Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf deucebucket/Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF:Q3_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default deucebucket/Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF:Q3_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use deucebucket/Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf deucebucket/Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF:Q3_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "deucebucket/Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF:Q3_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF / README.md

deucebucket

docs: add model-index evaluation metadata

8024940 verified 3 months ago

7.48 kB

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 (25-Shot)
          type: ai2_arc
          config: ARC-Challenge
          split: test
          args:
            num_few_shot: 25
        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 (10-Shot)
          type: hellaswag
          split: validation
          args:
            num_few_shot: 10
        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 (5-Shot)
          type: cais/mmlu
          config: all
          split: test
          args:
            num_few_shot: 5
        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

Gemma 4 26B-A4B-it Cerebellum GGUF

This repository contains GGUF builds derived from google/gemma-4-26B-A4B-it.

2026-05-22 Update

Added:

gemma-4-26B-A4B-it-cerebellum-v6.1-templatefix.gguf
sha256: d24229facdef8360a7ffa8b37a50e1de636b9139a5eba0efe899828e45ae7989

gemma-4-26b-a4b-it.mmproj.gguf
sha256: b762c43119ebdc3e3c36d929d958e827fac35b03278dda9203f87131aee1f185

The v6.1 file keeps the v6 tensor allocation and updates GGUF/runtime-facing metadata for Gemma 4 chat-template use. The update was tested with llama-server --jinja --reasoning auto and request-level no-thinking controls.

Older files in this repository are retained for reproducibility.

Tested Runtime

Runtime used for the 2026-05-22 templatefix checks:

llama.cpp fork: https://github.com/deucebucket/llama.cpp
branch: cerebellum/gemma4-runtime-fixes
fork commit: ded491334 fix: harden Gemma 4 server budgets
base build: b8930-59fa0b455

Server shape used locally:

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

Normal no-thinking requests used:

{
  "chat_template_kwargs": {"enable_thinking": false},
  "thinking_budget_tokens": 0
}

Bounded-thinking smoke requests used thinking_budget_tokens: 128.

2026-05-22 Templatefix Test Artifacts

Creative-writing smoke files:

creative_eval_20260522/regular_v6_1_templatefix_creative_summary.json
creative_eval_20260522/regular_v6_1_templatefix_creative_rerun_longcaps_summary.json

Non-coding tool-use files:

agentic_eval_20260522/README.md
agentic_eval_20260522/regular_v6_1_noncoding_agentic_tools_strict_summary.json

Observed 2026-05-22 results from those artifacts:

Area	Harness	Observed result
No-thinking output channel	six creative prompts	`reasoning_len=0` in recorded outputs
Template leakage markers	six creative prompts	no `<think>` marker or template marker recorded by checker
Creative long-cap rerun	four prompts rerun after initial length caps	four stop finishes in rerun summary
Non-coding tool workflow	three strict OpenAI-style tool tasks	`schedule_strict`, `release_notes_strict`, `creative_brief_strict` listed in `pass_cases`

The non-coding tool harness used mock tools named list_calendar, create_calendar_hold, search_notes, save_note, and add_task. It did not test code editing.

Evaluation

Benchmark results for the Cerebellum v6 tensor allocation, measured directly on the GGUF with llama.cpp llama-server on an RTX 3090. The v6.1 templatefix file keeps the v6 tensor allocation with zero tensor changes (metadata-only update), so these measurements describe the same weights. Summary JSONs are in benchmark_results/ in this repository.

Benchmark	Cerebellum v6 (11 GB)	Local Q3_K_M baseline
ARC-Challenge	95.56% (1172 q)	95.22%
HellaSwag	84.55% (10042 q)	86.57%
MMLU-Redux	71.33% (2400 q)	73.67%

Protocol: multiple-choice benchmarks run against a local llama-server with the project benchmark harness at temperature 0. HumanEval is not listed in the metadata because the retained v6 HumanEval artifacts are marked for audit in local notes. For Gemma 4, the current HumanEval/EvalPlus protocol uses the chat-completions harness (scripts/benchmark_evalplus_chat.py) with enable_thinking: false, thinking_budget_tokens: 0, and BENCH_WORKERS=1, not raw completions.

Historical Same-Repo Benchmark Artifacts

The following benchmark artifacts are from the earlier v6 line and the local Q3_K_M baseline. They are included as historical same-project measurements, not as new v6.1 measurements.

Artifact set	ARC-Challenge	HellaSwag	MMLU-Redux	HumanEval note
`q3km_baseline_*`	95.2218	86.5664	73.6667	`q3km_baseline_humaneval_results.json`: 62.2 pass@1
`cerebellum_v6_*`	95.5631	84.55	71.3333	v6 HumanEval artifacts are retained but marked for audit in local notes

For Gemma 4 HumanEval/EvalPlus, the local protocol now uses chat completions, not raw completions:

llama-server --jinja --reasoning auto
chat_template_kwargs: {"enable_thinking": false}
thinking_budget_tokens: 0
BENCH_WORKERS=1

Files and Provenance

Main v6.1 GGUF:

source base: google/gemma-4-26B-A4B-it
quantization family: mixed-precision GGUF
recipe lineage: Cerebellum v6 tensor allocation
base quant lineage: Q3_K_M with bartowski imatrix

Matching mmproj:

gemma-4-26b-a4b-it.mmproj.gguf

Notes

The 2026-05-22 tests were run on local llama-server.
The opencode coding-agent test is not used as a model-card result. In one internal White and Black project run, the model connected through the harness and ran a Godot test, then produced malformed edit-tool calls.
The creative-writing checks are smoke tests plus mechanical checks, not a human preference benchmark.
The non-coding tool checks use mocked tools and fixed task definitions.

Credits

Base model: Google Gemma Team, google/gemma-4-26B-A4B-it
Imatrix source used in the v6 lineage: bartowski, bartowski/google_gemma-4-26B-A4B-it-GGUF
GGUF/runtime: llama.cpp
Method and quantization workflow: deucebucket/osmosis Cerebellum pipeline
Local test artifacts: deucebucket Cerebellum workflow