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.

Libraries

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

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="deucebucket/Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF",
	filename="gemma-4-26B-A4B-it-cerebellum-v6.1-templatefix.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

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

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf deucebucket/Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf deucebucket/Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf deucebucket/Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf deucebucket/Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF:F16

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:F16
# Run inference directly in the terminal:
./llama-cli -hf deucebucket/Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF:F16

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:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf deucebucket/Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF:F16

Use Docker

docker model run hf.co/deucebucket/Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF:F16

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:F16

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:F16
```

Unsloth Studio

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

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for deucebucket/Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for deucebucket/Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for deucebucket/Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF to start chatting

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-server -hf deucebucket/Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF:F16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/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:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

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-server -hf deucebucket/Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF:F16

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:F16

Run Hermes

hermes

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:F16
```

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:F16

Run and chat with the model

lemonade run user.Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF-F16

List all available models

lemonade list

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

deucebucket

docs: restore attribution in templatefix card

3db5268 verified 16 days ago

preview code

raw

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metadata

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

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.

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