Instructions to use deucebucket/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-Cerebellum-GGUF:Q3_K_M
# Run inference directly in the terminal:
llama cli -hf deucebucket/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-Cerebellum-GGUF:Q3_K_M
# Run inference directly in the terminal:
llama cli -hf deucebucket/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-Cerebellum-GGUF:Q3_K_M
# Run inference directly in the terminal:
./llama-cli -hf deucebucket/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-Cerebellum-GGUF:Q3_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf deucebucket/Qwen3.6-35B-A3B-Cerebellum-GGUF:Q3_K_M

Use Docker

docker model run hf.co/deucebucket/Qwen3.6-35B-A3B-Cerebellum-GGUF:Q3_K_M

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:Q3_K_M

Ollama
How to use deucebucket/Qwen3.6-35B-A3B-Cerebellum-GGUF with Ollama:
```
ollama run hf.co/deucebucket/Qwen3.6-35B-A3B-Cerebellum-GGUF:Q3_K_M
```
Unsloth Desktop

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 serve -hf deucebucket/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-Cerebellum-GGUF:Q3_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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:Q3_K_M
```

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:Q3_K_M

Run and chat with the model

lemonade run user.Qwen3.6-35B-A3B-Cerebellum-GGUF-Q3_K_M

List all available models

lemonade list

Hermes Agent

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 serve -hf deucebucket/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-Cerebellum-GGUF:Q3_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use deucebucket/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-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

Qwen 3.6 35B-A3B — Cerebellum GGUF

Sensitivity-guided mixed-precision quantization of Qwen/Qwen3.6-35B-A3B. Cerebellum measures which weight groups survive extreme compression and which don't, then writes a single GGUF with per-tensor precision assignments — a standard GGUF that runs on stock llama.cpp, no fork.

Variant	File	Size	BPW	Best for
14 GB (recommended)	`Qwen3.6-35B-A3B-Cerebellum-14GB.gguf`	14.0 GB	3.34	best coding, 160K+ context
v3 (smallest)	`Qwen3.6-35B-A3B-Cerebellum-v3-Q3_K_M.gguf`	11 GB	2.76	tightest VRAM, vision

Evaluations

Coding — upstream EvalPlus (evalplus.codegen against llama-server, greedy / temp 0, n=164), same protocol across the size ladder:

build	size	HumanEval	HumanEval+
micro	11.96 GB	90.9	87.2
14 GB (recommended)	14.0 GB	93.3	90.2
uniform Q3_K_M	16.0 GB	91.5	89.0
Base	17.3 GB	92.7	89.0

Long-context: needle recall passes to 90K+ (verify-stress). Throughput: ~168 tok/s decode (3B-active MoE); fits 160K+ context at ~19 GB on a 24 GB card. Per-question artifacts in benchmark_results/14gb/.

Why the 14 GB over v3

v3 (11 GB) is the tightest-VRAM build. The 14 GB spends ~3 GB more to promote the routed ffn_down_exps to Q4_K — the group the ablation identifies as where the quality lives — and that gives it the family's best coding plus 160K+ context headroom. It posts above the 16 GB uniform Q3_K_M (−2 GB) and matches the 17.3 GB Base (−3.3 GB): the Base's extra promotions buy ~0 coding, so 14 GB is the efficient point. Pick v3 only when VRAM is tight or you need the vision projector.

Usage

# 14 GB (recommended)
llama-server -m Qwen3.6-35B-A3B-Cerebellum-14GB.gguf -ngl 99 -fa on --reasoning off

# v3 (smallest, with vision)
llama-server -m Qwen3.6-35B-A3B-Cerebellum-v3-Q3_K_M.gguf --mmproj mmproj-F16.gguf -ngl 99 -c 8192

Files

File	Size	Notes
`Qwen3.6-35B-A3B-Cerebellum-14GB.gguf`	14 GB	recommended — best coding, 160K+ ctx
`Qwen3.6-35B-A3B-Cerebellum-v3-Q3_K_M.gguf`	11 GB	smallest; vision (with mmproj)
`mmproj-F16.gguf`	858 MB	vision projector (F16)
`benchmark_results/`	—	per-question evaluation artifacts
`ablation/`	—	ablation logs + tensor override maps

Methodology

Built with Cerebellum — sensitivity-guided mixed-precision quantization: crush each tensor group, measure the impact, allocate precision under a size budget, output a plain GGUF. imatrix-calibrated. Quantized by @deucebucket.

Independent records

This line has a recorded data point in club-3090's BENCHMARKS (author-rig numbers from a full report.sh --full chain). The same report corrected their engine-support table for this model (issue #390, PR #393). Numbers there are author-reported, not club-validated.

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GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

3-bit

View +1 variant

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

Base model

Qwen/Qwen3.6-35B-A3B

Quantized

(777)

this model

Evaluation results

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

0.958
accuracy on HellaSwag
validation set Local benchmark run (RTX 3090, llama.cpp)

0.923
accuracy on MMLU-Redux
test set Local benchmark run (RTX 3090, llama.cpp)

0.750
pass@1 on HumanEval+ (pass@1)
test set Local benchmark run (RTX 3090, llama.cpp)

0.902