Instructions to use CISCai/gemma-4-31B-it-NVFP4-turbo-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CISCai/gemma-4-31B-it-NVFP4-turbo-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="CISCai/gemma-4-31B-it-NVFP4-turbo-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("CISCai/gemma-4-31B-it-NVFP4-turbo-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use CISCai/gemma-4-31B-it-NVFP4-turbo-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 CISCai/gemma-4-31B-it-NVFP4-turbo-GGUF:NVFP4
# Run inference directly in the terminal:
llama cli -hf CISCai/gemma-4-31B-it-NVFP4-turbo-GGUF:NVFP4

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf CISCai/gemma-4-31B-it-NVFP4-turbo-GGUF:NVFP4
# Run inference directly in the terminal:
llama cli -hf CISCai/gemma-4-31B-it-NVFP4-turbo-GGUF:NVFP4

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 CISCai/gemma-4-31B-it-NVFP4-turbo-GGUF:NVFP4
# Run inference directly in the terminal:
./llama-cli -hf CISCai/gemma-4-31B-it-NVFP4-turbo-GGUF:NVFP4

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 CISCai/gemma-4-31B-it-NVFP4-turbo-GGUF:NVFP4
# Run inference directly in the terminal:
./build/bin/llama-cli -hf CISCai/gemma-4-31B-it-NVFP4-turbo-GGUF:NVFP4

Use Docker

docker model run hf.co/CISCai/gemma-4-31B-it-NVFP4-turbo-GGUF:NVFP4

LM Studio
Jan

vLLM

How to use CISCai/gemma-4-31B-it-NVFP4-turbo-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CISCai/gemma-4-31B-it-NVFP4-turbo-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": "CISCai/gemma-4-31B-it-NVFP4-turbo-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CISCai/gemma-4-31B-it-NVFP4-turbo-GGUF:NVFP4

SGLang

How to use CISCai/gemma-4-31B-it-NVFP4-turbo-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "CISCai/gemma-4-31B-it-NVFP4-turbo-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CISCai/gemma-4-31B-it-NVFP4-turbo-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "CISCai/gemma-4-31B-it-NVFP4-turbo-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CISCai/gemma-4-31B-it-NVFP4-turbo-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use CISCai/gemma-4-31B-it-NVFP4-turbo-GGUF with Ollama:
```
ollama run hf.co/CISCai/gemma-4-31B-it-NVFP4-turbo-GGUF:NVFP4
```
Unsloth Desktop

How to use CISCai/gemma-4-31B-it-NVFP4-turbo-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf CISCai/gemma-4-31B-it-NVFP4-turbo-GGUF:NVFP4

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": "CISCai/gemma-4-31B-it-NVFP4-turbo-GGUF:NVFP4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use CISCai/gemma-4-31B-it-NVFP4-turbo-GGUF with Docker Model Runner:
```
docker model run hf.co/CISCai/gemma-4-31B-it-NVFP4-turbo-GGUF:NVFP4
```

Lemonade

How to use CISCai/gemma-4-31B-it-NVFP4-turbo-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull CISCai/gemma-4-31B-it-NVFP4-turbo-GGUF:NVFP4

Run and chat with the model

lemonade run user.gemma-4-31B-it-NVFP4-turbo-GGUF-NVFP4

List all available models

lemonade list

Hermes Agent

How to use CISCai/gemma-4-31B-it-NVFP4-turbo-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 CISCai/gemma-4-31B-it-NVFP4-turbo-GGUF:NVFP4

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 CISCai/gemma-4-31B-it-NVFP4-turbo-GGUF:NVFP4

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use CISCai/gemma-4-31B-it-NVFP4-turbo-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf CISCai/gemma-4-31B-it-NVFP4-turbo-GGUF:NVFP4

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 "CISCai/gemma-4-31B-it-NVFP4-turbo-GGUF:NVFP4" \
  --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"

gemma-4-31B-it-NVFP4-turbo-GGUF / README.md

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	---

	license: apache-2.0
	license_link: https://ai.google.dev/gemma/docs/gemma_4_license
	library_name: transformers
	base_model:
	- LilaRest/gemma-4-31B-it-NVFP4-turbo
	pipeline_tag: text-generation
	tags:
	- gemma4
	- gemma-4-31b-it
	- nvfp4
	- modelopt
	- vllm
	- quantized
	- nvidia
	- lighthouse
	model-index:
	- name: gemma-4-31B-it-NVFP4-turbo
	results:
	- task:
	type: text-generation
	dataset:
	name: GPQA Diamond
	type: Idavidrein/gpqa
	config: gpqa_diamond
	metrics:
	- name: Accuracy
	type: accuracy
	value: 72.73
	- task:
	type: text-generation
	dataset:
	name: MMLU Pro
	type: TIGER-Lab/MMLU-Pro
	metrics:
	- name: Accuracy
	type: accuracy
	value: 83.93

	---

	<div align="center">
	<img src="https://huggingface.co/LilaRest/gemma-4-31B-it-NVFP4-turbo/resolve/main/banner.png">
	</div>

	<h1 align="center">⚡ Gemma 4 31B IT NVFP4 <i>Turbo</i> GGUF</h1>

	Requires [ggml-org/llama.cpp#21971](https://github.com/ggml-org/llama.cpp/pull/21971)

	A repackaged [nvidia/Gemma-4-31B-IT-NVFP4](https://huggingface.co/nvidia/Gemma-4-31B-IT-NVFP4) that is 68% smaller in GPU memory and ~2.5× faster than the [base model](https://huggingface.co/google/gemma-4-31B-it), while retaining nearly identical quality (1-3% loss). Fits on a single RTX 5090 (🎉).

	## Approach

	Three changes were made:

	1. Quantized all self-attention weights from BF16 → FP4 (RTN, group_size=16, matching modelopt NVFP4 format)
	2. Updated architecture to `Gemma4ForCausalLM` and quantization config accordingly
	3. Stripped the vision and audio encoder

	Everything else is untouched — MLP layers keep NVIDIA's calibrated FP4, `embed_tokens` stays BF16, all norms preserved, so we retain all the [nvidia/Gemma-4-31B-IT-NVFP4](https://huggingface.co/nvidia/Gemma-4-31B-IT-NVFP4) optimizations.

	#### Why RTN didn't hurt quality

	RTN (Round-To-Nearest) is the simplest quantization method — no calibration data, fully reproducible. It worked here because:

	- FP4 with group_size=16 and per-group scaling preserves relative weight distributions well
	- Self-attention weights tend to be normally distributed near zero, where the FP4 grid has finest resolution (0, 0.5, 1.0, 1.5)
	- MLP layers (more sensitive to quantization) keep NVIDIA's calibrated FP4
	- `embed_tokens` stays BF16, preventing noise from propagating through all layers

	## License

	Apache 2.0 — same as the [base model](https://ai.google.dev/gemma/docs/gemma_4_license).

	## Credits

	- [Google DeepMind](https://deepmind.google/models/gemma/) for Gemma 4
	- [NVIDIA](https://huggingface.co/nvidia/Gemma-4-31B-IT-NVFP4) for the modelopt NVFP4 checkpoint