Instructions to use deucebucket/Gemma-4-26B-A4B-it-Cerebellum-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-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-GGUF:Q3_K_M
# Run inference directly in the terminal:
llama cli -hf deucebucket/Gemma-4-26B-A4B-it-Cerebellum-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-GGUF:Q3_K_M
# Run inference directly in the terminal:
llama cli -hf deucebucket/Gemma-4-26B-A4B-it-Cerebellum-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-GGUF:Q3_K_M
# Run inference directly in the terminal:
./llama-cli -hf deucebucket/Gemma-4-26B-A4B-it-Cerebellum-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-GGUF:Q3_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf deucebucket/Gemma-4-26B-A4B-it-Cerebellum-GGUF:Q3_K_M

Use Docker

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

LM Studio
Jan

vLLM

How to use deucebucket/Gemma-4-26B-A4B-it-Cerebellum-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-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-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-GGUF:Q3_K_M

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

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Hermes Agent

How to use deucebucket/Gemma-4-26B-A4B-it-Cerebellum-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-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-GGUF:Q3_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use deucebucket/Gemma-4-26B-A4B-it-Cerebellum-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-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-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"

Cerebellum

Gemma 4 26B-A4B-it -- Cerebellum GGUF

Current release: v5.

Cerebellum v5 is an ablation-guided mixed-precision GGUF quantization of google/gemma-4-26B-A4B-it.

This is a 26B-parameter MoE model with 4B active parameters per token and 128 experts per layer. This release uses tensor-level precision overrides selected from 110+ ablation experiments across five internal iterations.

At a Glance

Current file: Gemma-4-26B-A4B-it-Cerebellum-Q3_K_M.gguf
Current release: v5
File size: 11 GB
Base model: google/gemma-4-26B-A4B-it
Format: GGUF, mixed precision, imatrix-guided
Test hardware: RTX 3090, llama.cpp

Evaluation

Measured on RTX 3090 with llama.cpp at temperature 0. HumanEval below is the legacy raw-completions number; for Gemma 4 the current protocol is the chat-completions harness (raw completions understate Gemma 4 coding), so treat it as legacy. Summary JSONs are in benchmark_results/.

Benchmark	v5 Result
WikiText PPL	12,356
HumanEval pass@1	71.3%
ARC-Challenge	95.4%
HellaSwag	84.7%
MMLU-Redux	71.4%

All benchmark results above were measured on the same local setup: RTX 3090 and llama.cpp. PPL was measured on the WikiText-2 test set with 2048 context and 128 chunks.

PPL is high in absolute terms for this model and appears consistent across Gemma 4 26B quant levels tested locally.

Version History

Version	PPL	HumanEval	What Changed
v1	20,614	65.2%	Group-level ablation only
v2	19,826	65.9%	+ attn_q layer ablation (30 layers)
v3	19,826	67.1%	+ PLE (norms/scales) protection
v4	12,614	69.5%	+ ffn_up layer ablation + precision rebalance
v5	12,356	71.3%	+ attn_k layer ablation (30 layers, 7 promoted)

Usage

llama-server -m Gemma-4-26B-A4B-it-Cerebellum-Q3_K_M.gguf \
  -ngl 99 -c 24576 --jinja --reasoning auto

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). Fits in 24 GB VRAM at full GPU offload.

How Cerebellum Works

Cerebellum uses ablation testing to assign different quantization types to different tensor groups and layers.

Level 1: Group Ablation

Each tensor category was tested at Q2_K and measured by perplexity impact:

Group	Tensors	PPL Delta	Action
attn_q	30	+13.4%	Tested per layer
ffn_gate	30	-1.2%	Left at Q3_K
expert_gate_up	30 x 507M	-5.5%	Set to Q2_K
attn_k	30	-12.1%	Tested per layer
ffn_up	30	-18.2%	Set to Q2_K

Three of five tested groups had lower measured PPL at Q2_K in this setup.

Level 2: Layer Ablation (attn_q)

The attn_q group had a +13.4% PPL delta at Q2_K. All 30 layers were then tested individually. No individual layer exceeded +3.9%, so v5 keeps 9 of 30 layers at Q5_K.

Level 3: Reverse Layer Ablation (attn_k)

v4 had all attn_k layers at Q2_K. Each layer was tested individually at Q3_K:

4 layers (5, 11, 16, 29) had higher PPL at Q3_K and stayed Q2_K.
7 layers (1, 6, 17, 18, 23, 24, 28) had lower PPL at Q3_K.
Layer 23 changed by -3.8%; layer 18 changed by -2.8%.

Those 7 layer changes are included in v5.

Router/Gate Investigation

We also tested the MoE routing tensors:

Test	What	Result
Crush all routers (F32 to Q2_K)	30 ffn_gate_inp tensors	+30.7% PPL
Promote all gates (Q3_K to Q6_K)	30 ffn_gate tensors	+36.1% PPL

The v5 precision map leaves router tensors at F32 and gate tensors at Q3_K.

Final Precision Map (v5)

Tensor Type	Precision	Count	Rationale
attn_q (9 sensitive layers)	Q5_K	9	Layer-validated critical
attn_k (7 promoted layers)	Q3_K	7	Reverse ablation: improve when promoted
attn_k (remaining)	Q2_K	23	Group-level demotable
ffn_up	Q2_K	30	Group PPL delta: -18.2%
expert_gate_up	Q2_K	30	Demotable (-5.5%)
ffn_gate	Q3_K	30	Tolerant (-1.2%)
ffn_gate_inp (router)	F32	30	Group PPL delta: +30.7% when set to Q2_K
Norms, scales	F32	392	Structural -- always full precision

91 tensor-level overrides on top of Q3_K_M base with bartowski's imatrix.

Technical Details

Architecture: Gemma 4 26B -- 26B total params, 4B active per token, 128 experts/layer, 30 layers
Base quant: Q3_K_M with bartowski imatrix
Ablation experiments: 110+ total across 5 iterations
Quantizer: llama.cpp llama-quantize with --tensor-type-file overrides
Hardware: RTX 3090 (24 GB VRAM)