Instructions to use deucebucket/Granite-4.1-30B-Cerebellum-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use deucebucket/Granite-4.1-30B-Cerebellum-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="deucebucket/Granite-4.1-30B-Cerebellum-GGUF",
	filename="Granite-4.1-30B-Cerebellum-v1.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use deucebucket/Granite-4.1-30B-Cerebellum-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/Granite-4.1-30B-Cerebellum-GGUF
# Run inference directly in the terminal:
llama-cli -hf deucebucket/Granite-4.1-30B-Cerebellum-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf deucebucket/Granite-4.1-30B-Cerebellum-GGUF
# Run inference directly in the terminal:
llama-cli -hf deucebucket/Granite-4.1-30B-Cerebellum-GGUF

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/Granite-4.1-30B-Cerebellum-GGUF
# Run inference directly in the terminal:
./llama-cli -hf deucebucket/Granite-4.1-30B-Cerebellum-GGUF

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/Granite-4.1-30B-Cerebellum-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf deucebucket/Granite-4.1-30B-Cerebellum-GGUF

Use Docker

docker model run hf.co/deucebucket/Granite-4.1-30B-Cerebellum-GGUF

LM Studio
Jan
Ollama
How to use deucebucket/Granite-4.1-30B-Cerebellum-GGUF with Ollama:
```
ollama run hf.co/deucebucket/Granite-4.1-30B-Cerebellum-GGUF
```

Unsloth Studio

How to use deucebucket/Granite-4.1-30B-Cerebellum-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/Granite-4.1-30B-Cerebellum-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/Granite-4.1-30B-Cerebellum-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/Granite-4.1-30B-Cerebellum-GGUF to start chatting

How to use deucebucket/Granite-4.1-30B-Cerebellum-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/Granite-4.1-30B-Cerebellum-GGUF

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/Granite-4.1-30B-Cerebellum-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use deucebucket/Granite-4.1-30B-Cerebellum-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/Granite-4.1-30B-Cerebellum-GGUF

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/Granite-4.1-30B-Cerebellum-GGUF

Run Hermes

hermes

Docker Model Runner
How to use deucebucket/Granite-4.1-30B-Cerebellum-GGUF with Docker Model Runner:
```
docker model run hf.co/deucebucket/Granite-4.1-30B-Cerebellum-GGUF
```

Lemonade

How to use deucebucket/Granite-4.1-30B-Cerebellum-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull deucebucket/Granite-4.1-30B-Cerebellum-GGUF

Run and chat with the model

lemonade run user.Granite-4.1-30B-Cerebellum-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Granite 4.1-30B — Cerebellum GGUF

Ablation-guided mixed-precision quantization of ibm-granite/granite-4.1-30b. 30B parameters, dense architecture with GQA, 64 layers.

What is Cerebellum?

Instead of uniform quantization, we measure which weight groups survive aggressive compression and which don't. Groups that tolerate Q2_K get demoted; groups that don't stay at Q3_K_M or higher. The result: smaller files with less quality loss than uniform quants of the same size.

Files

File	Size	Description
`Granite-4.1-30B-Cerebellum-v2.gguf`	13 GB	Optimal mix — 3 groups demoted (attn_k, attn_q, attn_output), 4 kept at Q3_K_M
`Granite-4.1-30B-Cerebellum-v1.gguf`	12 GB	Aggressive — 5 groups demoted (all attn + ffn_gate)

Benchmarks

Evaluated using our standardized benchmark suite (ARC-Challenge, HellaSwag, MMLU, HumanEval) with temperature=0, no thinking mode.

Cerebellum v2 (13 GB) — Recommended

Benchmark	Score	Questions
ARC-Challenge	91.6%	1,172
HellaSwag	88.9%	10,042
MMLU	73.5%	11,643
HumanEval	82.3%	164

Size vs Quality

Model	Size	BPW	PPL (wiki)
Q3_K_M (baseline)	14 GB	3.94	8.3736
Cerebellum v2	13 GB	3.76	8.4912
Cerebellum v1	12 GB	3.50	9.1405

v2 saves 1 GB (7%) over Q3_K_M with only +1.4% perplexity increase — and the 3 demoted groups actually improved perplexity individually during ablation.

Methodology

Group ablation: Demote each of 7 weight groups (attn_k, attn_q, attn_v, attn_output, ffn_gate, ffn_up, ffn_down) to Q2_K individually. Measure PPL impact.
Identify improvers: Three groups (attn_k, attn_q, attn_output) showed lower PPL when demoted — the Q3_K_M precision was actually hurting these layers.
Build optimal mix: v2 demotes only the 3 groups that improve; v1 additionally demotes attn_v and ffn_gate.

Ablation Results

Group	PPL when demoted	Delta vs baseline
attn_k	8.1639	-0.2097 (improved!)
attn_q	8.3144	-0.0592 (improved!)
attn_output	8.3539	-0.0197 (improved!)
attn_v	8.4713	+0.0977
ffn_gate	8.5967	+0.2231
ffn_up	9.0587	+0.6851
ffn_down	9.0190	+0.6454

v2 Override Map

Demoted (Q2_K): attn_k, attn_q, attn_output (all 64 layers)
Sacred (kept at Q3_K_M): attn_v, ffn_gate, ffn_up, ffn_down

Usage

Works with any llama.cpp-compatible tool:

# llama.cpp
./llama-server --model Granite-4.1-30B-Cerebellum-v2.gguf -ngl 99 --ctx-size 4096

# Ollama (create Modelfile pointing to the GGUF)
# LM Studio (drag and drop)
# koboldcpp, text-generation-webui, etc.

Hardware Requirements

v2 (13 GB): Fits in 16 GB VRAM with room for context. RTX 4060 Ti 16GB, RTX 3090, etc.
v1 (12 GB): Fits in 16 GB VRAM with generous context, or tight in 12 GB.

Credits

Quantized with Cerebellum — ablation-guided mixed-precision quantization by deucebucket.
Base model by IBM Granite.

Downloads last month: 80

GGUF

Model size

29B params

Architecture

granite

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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Model tree for deucebucket/Granite-4.1-30B-Cerebellum-GGUF

Base model

ibm-granite/granite-4.1-30b

Quantized

(28)

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