Instructions to use deucebucket/Qwen3.6-35B-A3B-Cerebellum-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use deucebucket/Qwen3.6-35B-A3B-Cerebellum-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="deucebucket/Qwen3.6-35B-A3B-Cerebellum-GGUF",
	filename="Qwen3.6-35B-A3B-Cerebellum-v3.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/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-Cerebellum-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf deucebucket/Qwen3.6-35B-A3B-Cerebellum-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf deucebucket/Qwen3.6-35B-A3B-Cerebellum-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf deucebucket/Qwen3.6-35B-A3B-Cerebellum-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/Qwen3.6-35B-A3B-Cerebellum-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf deucebucket/Qwen3.6-35B-A3B-Cerebellum-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/Qwen3.6-35B-A3B-Cerebellum-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf deucebucket/Qwen3.6-35B-A3B-Cerebellum-GGUF:F16

Use Docker

docker model run hf.co/deucebucket/Qwen3.6-35B-A3B-Cerebellum-GGUF:F16

LM Studio
Jan

vLLM

How to use deucebucket/Qwen3.6-35B-A3B-Cerebellum-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "deucebucket/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-Cerebellum-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/deucebucket/Qwen3.6-35B-A3B-Cerebellum-GGUF:F16

Ollama
How to use deucebucket/Qwen3.6-35B-A3B-Cerebellum-GGUF with Ollama:
```
ollama run hf.co/deucebucket/Qwen3.6-35B-A3B-Cerebellum-GGUF:F16
```

Unsloth Studio

How to use deucebucket/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-Cerebellum-GGUF to start chatting

How to use deucebucket/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-Cerebellum-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/Qwen3.6-35B-A3B-Cerebellum-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use deucebucket/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-Cerebellum-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/Qwen3.6-35B-A3B-Cerebellum-GGUF:F16

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use deucebucket/Qwen3.6-35B-A3B-Cerebellum-GGUF with Docker Model Runner:
```
docker model run hf.co/deucebucket/Qwen3.6-35B-A3B-Cerebellum-GGUF:F16
```

Lemonade

How to use deucebucket/Qwen3.6-35B-A3B-Cerebellum-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull deucebucket/Qwen3.6-35B-A3B-Cerebellum-GGUF:F16

Run and chat with the model

lemonade run user.Qwen3.6-35B-A3B-Cerebellum-GGUF-F16

List all available models

lemonade list

Qwen 3.6 35B-A3B — Cerebellum GGUF

Sensitivity-guided mixed-precision quantization of Qwen/Qwen3.6-35B-A3B. Two variants available:

Variant	File	Size	BPW
Cerebellum v3 (recommended)	`Qwen3.6-35B-A3B-Cerebellum-v3.gguf`	11 GB	2.76
Cerebellum v1 (legacy)	`Qwen3.6-35B-A3B-Cerebellum.gguf`	12 GB	2.73

Cerebellum measures which weight groups survive extreme compression and which don't, then writes a single GGUF with per-tensor precision assignments. v3 uses 360 tensor-level overrides guided by group ablation and reverse ablation analysis.

Benchmarks

All benchmarks measured directly on these GGUF files using llama.cpp inference with cleaned evaluation harness.

Benchmark	v3 (11 GB)	v1 (12 GB)	Q3_K_M (15.6 GB)
ARC-Challenge	95.8%	94.8%	96.1%
HellaSwag	92.3%	91.5%	91.5%
MMLU-Redux	75.0%	73.9%	74.1%
HumanEval base	70.7%	—	64.0%
HumanEval+	65.2%	—	56.7%
Vision smoke (36 images)	100%	100%	—

v3 at 11 GB is 29% smaller than stock Q3_K_M (15.6 GB) while outperforming it on 5 of 6 benchmarks. The Q2_K regularization effect on gate/mixing weights actively improves downstream task performance despite reducing perplexity.

v3 Allocation

Group	Precision	Rationale
`attn_qkv`	Q3_K_M	Critical for vision and attention routing
`ssm_out`	Q3_K_M	Most sensitive tensor per ablation (+0.24 PPL)
`ffn_gate_exps`	Q2_K	Q2_K regularization outperforms Q3_K_M
`ffn_up_exps`	Q2_K	Q2_K regularization outperforms Q3_K_M
`ffn_down_exps`	Q2_K	Acceptable loss for size savings
`ffn_gate_shexp`	Q2_K	Q2_K regularization outperforms Q3_K_M
`ffn_up_shexp`	Q2_K	Q2_K regularization outperforms Q3_K_M
`ffn_down_shexp`	Q2_K	Q2_K regularization outperforms Q3_K_M
`attn_gate`	Q2_K	Q2_K regularization outperforms Q3_K_M
`ssm_alpha`, `ssm_beta`	Q2_K	Q2_K regularization outperforms Q3_K_M

Protected: all norms (F32), SSM state params (F32), router tensors (default).

Ablation Data

Full ablation methodology and results are in the ablation/ directory:

group_ablation_results.log — Forward ablation: demote each group to Q2_K, measure PPL
reverse_ablation_results.log — Reverse ablation: from fully-demoted v1, restore each group
cerebellum_v3_overrides.txt — The 360-line tensor type override file used for v3

Key finding from reverse ablation: 6 of 10 groups perform better at Q2_K than Q3_K_M — imatrix-guided Q2_K acts as beneficial regularization on gate, mixing, and shared expert weights.

Usage

# v3 (recommended, 11 GB)
llama-server --model Qwen3.6-35B-A3B-Cerebellum-v3.gguf \
  --mmproj mmproj-F16.gguf --n-gpu-layers 99 --ctx-size 8192

# v1 (legacy, 12 GB)
llama-server --model Qwen3.6-35B-A3B-Cerebellum.gguf \
  --mmproj mmproj-F16.gguf --n-gpu-layers 99 --ctx-size 8192

Files

File	Size	Description
`Qwen3.6-35B-A3B-Cerebellum-v3.gguf`	11 GB	v3 — recommended, 29% smaller than Q3_K_M
`Qwen3.6-35B-A3B-Cerebellum.gguf`	12 GB	v1 — legacy
`mmproj-F16.gguf`	858 MB	Vision projection (F16)
`benchmark_results/v3/`	—	Full benchmark JSON artifacts for v3
`ablation/`	—	Ablation logs and override files

Methodology

Built with Cerebellum — sensitivity-guided mixed-precision quantization. v3 uses unsloth coder imatrix for importance-weighted quantization within each precision level.

Quantized by @deucebucket.

Downloads last month: 1,731

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for deucebucket/Qwen3.6-35B-A3B-Cerebellum-GGUF

Base model

Qwen/Qwen3.6-35B-A3B

Quantized

(453)

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