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

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	---
	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:

	```text
	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:

	```text
	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:

	```bash
	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:

	```json
	{
	"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:

	```text
	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:

	```text
	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:

	```text
	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:

	```text
	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:

	```text
	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