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-Q3_K_M.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: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-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

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 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:Q3_K_M

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:Q3_K_M"
        }
      ]
    }
  }
}

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: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 new
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

Cerebellum

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-Q3_K_M.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.

Measured launch (RTX 3090, llama.cpp)

Measured 2026-06-13 on a single RTX 3090 (24 GB), one llama-server, KV cache q8_0:

metric	measured
decode speed	123 tok/s
peak VRAM (4-slot serving)	15.1 GB
max measured context (q8_0 KV)	131,072

llama-server -m gemma-4-26B-A4B-it-cerebellum-v6.1-templatefix-Q3_K_M.gguf \
  -ngl 99 --parallel 4 -c 24576 --jinja --reasoning-budget 0

This rig's measurements; no quality claims beyond them.

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-Q3_K_M.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

Downloads last month: 1,989

GGUF

Model size

25B params

Architecture

gemma4

Hardware compatibility

3-bit

Model tree for deucebucket/Gemma-4-26B-A4B-it-Cerebellum-v6-GGUF

Base model

google/gemma-4-26B-A4B

Finetuned

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

Quantized

(264)

this model

Evaluation results

normalized accuracy on AI2 Reasoning Challenge
test set Local audited benchmark run (RTX 3090, llama.cpp)

0.956
accuracy on HellaSwag
validation set Local audited benchmark run (RTX 3090, llama.cpp)

0.846
accuracy on MMLU-Redux
test set Local audited benchmark run (RTX 3090, llama.cpp)

0.713