Instructions to use satgeze/Gemma4-31B-Uncensored-HauhauCS-1M-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 satgeze/Gemma4-31B-Uncensored-HauhauCS-1M-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 satgeze/Gemma4-31B-Uncensored-HauhauCS-1M-GGUF
# Run inference directly in the terminal:
llama cli -hf satgeze/Gemma4-31B-Uncensored-HauhauCS-1M-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf satgeze/Gemma4-31B-Uncensored-HauhauCS-1M-GGUF
# Run inference directly in the terminal:
llama cli -hf satgeze/Gemma4-31B-Uncensored-HauhauCS-1M-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 satgeze/Gemma4-31B-Uncensored-HauhauCS-1M-GGUF
# Run inference directly in the terminal:
./llama-cli -hf satgeze/Gemma4-31B-Uncensored-HauhauCS-1M-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 satgeze/Gemma4-31B-Uncensored-HauhauCS-1M-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf satgeze/Gemma4-31B-Uncensored-HauhauCS-1M-GGUF

Use Docker

docker model run hf.co/satgeze/Gemma4-31B-Uncensored-HauhauCS-1M-GGUF

LM Studio
Jan

vLLM

How to use satgeze/Gemma4-31B-Uncensored-HauhauCS-1M-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "satgeze/Gemma4-31B-Uncensored-HauhauCS-1M-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": "satgeze/Gemma4-31B-Uncensored-HauhauCS-1M-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/satgeze/Gemma4-31B-Uncensored-HauhauCS-1M-GGUF

Ollama
How to use satgeze/Gemma4-31B-Uncensored-HauhauCS-1M-GGUF with Ollama:
```
ollama run hf.co/satgeze/Gemma4-31B-Uncensored-HauhauCS-1M-GGUF
```

Unsloth Studio

How to use satgeze/Gemma4-31B-Uncensored-HauhauCS-1M-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 satgeze/Gemma4-31B-Uncensored-HauhauCS-1M-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 satgeze/Gemma4-31B-Uncensored-HauhauCS-1M-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for satgeze/Gemma4-31B-Uncensored-HauhauCS-1M-GGUF to start chatting

How to use satgeze/Gemma4-31B-Uncensored-HauhauCS-1M-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf satgeze/Gemma4-31B-Uncensored-HauhauCS-1M-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": "satgeze/Gemma4-31B-Uncensored-HauhauCS-1M-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use satgeze/Gemma4-31B-Uncensored-HauhauCS-1M-GGUF with Docker Model Runner:
```
docker model run hf.co/satgeze/Gemma4-31B-Uncensored-HauhauCS-1M-GGUF
```

Lemonade

How to use satgeze/Gemma4-31B-Uncensored-HauhauCS-1M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull satgeze/Gemma4-31B-Uncensored-HauhauCS-1M-GGUF

Run and chat with the model

lemonade run user.Gemma4-31B-Uncensored-HauhauCS-1M-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use satgeze/Gemma4-31B-Uncensored-HauhauCS-1M-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 satgeze/Gemma4-31B-Uncensored-HauhauCS-1M-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 satgeze/Gemma4-31B-Uncensored-HauhauCS-1M-GGUF

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use satgeze/Gemma4-31B-Uncensored-HauhauCS-1M-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf satgeze/Gemma4-31B-Uncensored-HauhauCS-1M-GGUF

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 "satgeze/Gemma4-31B-Uncensored-HauhauCS-1M-GGUF" \
  --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"

Gemma4-31B Uncensored: 1M Context + MTP + Vision

HauhauCS/Gemma4-31B-QAT-Uncensored-HauhauCS-Balanced-MTP (31B dense, Google QAT checkpoint) with a 1,048,576-token context baked in (4x the native 262,144), shipping with its MTP speculative-decoding draft head and vision tower. All numbers below were measured on these exact files.

Capability	Status
1M context	Certified: 10/10 at every rung from 262K to 1M, f16 KV, on a single H200
MTP speculative decoding	69.2 to 101.0 tok/s (+46%), acceptance 0.658 (measured on this trunk, RTX 5090)
Vision	Verified July 6, 2026: reads image text and identifies objects
Uncensored	HauhauCS Balanced abliteration; trunk weights bit-identical to the source release

Needle-in-a-haystack: certified to 1,048,576 tokens

Full ladder, 10 needles per rung, depths 5 to 95 percent, temperature 0, f16 KV, scored July 9, 2026 on a single H200 (a dense 31B at f16 KV needs an 82 GB cache at 1M, beyond any 32 GB card):

Rung	Score
262,144	10/10
393,216	10/10
524,288	10/10
786,432	10/10
1,048,576	10/10

Raw evidence is in results.jsonl. The first Gemma 4 31B we know of that is needle-perfect at a million tokens.

MTP speculative decoding

The draft head predicts ahead and the trunk verifies every token, so output is identical to standard decoding, only faster. Measured speedup on this uncensored trunk beats the ~35 percent claimed upstream.

Files

File	Size	Role
`gemma4-31b-uncensored-1M-Q4.gguf`	18.7 GB	Trunk, 1M baked, QAT 4-bit
`mtp-gemma-31b.gguf`	280 MB	MTP draft head, pair with `-md`
`mmproj-gemma31b-hauhau.gguf`	1.2 GB	Vision tower, pair with `--mmproj`
`niah_heatmap.png`, `mtp_speedup.png`, `results.jsonl`	small	Verification evidence

Every file, every mirror

Nothing was discontinued: every quant is one click away. Hugging Face carries the curated picks, ModelScope always carries everything, and Ollama serves ready-to-run tags.

File	Size	Hugging Face	ModelScope	Ollama
`gemma4-31b-uncensored-1M-Q4.gguf`	18.7 GB	download	download	-
`mmproj-gemma31b-hauhau.gguf`	1.2 GB	download	download	-
`mtp-gemma-31b.gguf`	280 MB	download	download	-

Run it

llama.cpp, everything on:

llama-server -m gemma4-31b-uncensored-1M-Q4.gguf \
  -c 1048576 -np 1 --jinja \
  -md mtp-gemma-31b.gguf --spec-type draft-mtp --spec-draft-n-max 3 \
  --mmproj mmproj-gemma31b-hauhau.gguf

Ollama (1M and vision work; Ollama has no speculative decoding yet, so the MTP head adds no speed there):

FROM ./gemma4-31b-uncensored-1M-Q4.gguf
RENDERER gemma4
PARSER gemma4
PARAMETER num_ctx 262144

The RENDERER and PARSER lines avoid imported-GGUF template bugs under tool-heavy use. Raise num_ctx as memory allows.

How to actually use a 1M-context model

Long context is a capability, not a magic mode. Habits that measurably help (from our RULER and adherence testing across this fleet):

Re-state your standing instructions near the end of long prompts. Recency wins over depth; a short reinjection of the rules beats hoping the model remembers page one.
Prefer one big reference dump over a long accumulated conversation. Fresh session per task, context used as a library.
After any compaction or summarization step, repeat your active rules yourself.
For retrieval-heavy work on this model family, run thinking OFF (see the RULER table: thinking mode halves retrieval scores at long range).
Expect the extremes to cost: prefill at 500K+ takes real time on any hardware. Budget for it or stage your questions.

How this was built

YaRN rope-scaling metadata (factor 4.0 over native 262,144) baked into the GGUF header with gguf-py; weights are bit-identical to the HauhauCS release, no fine-tuning. Gemma 4's dual-rope design takes YaRN on its global-attention layers. Certification harness: 10 needles per rung at depths 5 to 95 percent, temperature 0, seeded prompts, f16 KV only. Method and tooling: github.com/satindergrewal/aviary-1m.

For base capability benchmarks see Google's official Gemma 4 cards; uncensoring quality versus the official trunk has not been independently benchmarked here.

Credits

Base model and QAT: Google (Gemma license; its terms flow down to these files). Uncensoring and packaging: HauhauCS. MTP head: Unsloth (via the HauhauCS repo). 1M YaRN extension, benchmarking, and certification: SatGeze.

Sister repos: 12B | 26B-A4B | 31B | Qwen3.6-35B

Mirrors: Hugging Face | ModelScope