Instructions to use Neohosseinism/gemma4-stack with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Neohosseinism/gemma4-stack 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 Neohosseinism/gemma4-stack:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Neohosseinism/gemma4-stack:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Neohosseinism/gemma4-stack:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Neohosseinism/gemma4-stack:Q4_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 Neohosseinism/gemma4-stack:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Neohosseinism/gemma4-stack:Q4_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 Neohosseinism/gemma4-stack:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Neohosseinism/gemma4-stack:Q4_K_M

Use Docker

docker model run hf.co/Neohosseinism/gemma4-stack:Q4_K_M

LM Studio
Jan
Ollama
How to use Neohosseinism/gemma4-stack with Ollama:
```
ollama run hf.co/Neohosseinism/gemma4-stack:Q4_K_M
```

Unsloth Studio

How to use Neohosseinism/gemma4-stack 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 Neohosseinism/gemma4-stack 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 Neohosseinism/gemma4-stack to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Neohosseinism/gemma4-stack to start chatting

How to use Neohosseinism/gemma4-stack with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Neohosseinism/gemma4-stack:Q4_K_M

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": "Neohosseinism/gemma4-stack:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Neohosseinism/gemma4-stack with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Neohosseinism/gemma4-stack:Q4_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 Neohosseinism/gemma4-stack:Q4_K_M

Run Hermes

hermes

OpenClaw new

How to use Neohosseinism/gemma4-stack with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Neohosseinism/gemma4-stack:Q4_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 "Neohosseinism/gemma4-stack:Q4_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"

Docker Model Runner
How to use Neohosseinism/gemma4-stack with Docker Model Runner:
```
docker model run hf.co/Neohosseinism/gemma4-stack:Q4_K_M
```

Lemonade

How to use Neohosseinism/gemma4-stack with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Neohosseinism/gemma4-stack:Q4_K_M

Run and chat with the model

lemonade run user.gemma4-stack-Q4_K_M

List all available models

lemonade list

Atomic Chat

gemma4-stack / scripts /bench.sh

Neohosseinism

Add gemma4-stack config, scripts, and docker-compose setup

89bf59d 23 days ago

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1.19 kB

	#!/usr/bin/env bash
	# Sweep llama-bench across thread counts on this machine to pick the best
	# --threads for a given Gemma 4 GGUF. Results saved under scripts/bench-results/.
	#
	# ./bench.sh /models/gemma-4-E4B-it-Q4_K_M.gguf
	# THREADS_LIST="2 4 8" ./bench.sh <gguf-path-inside-container>
	#
	# Requires the GGUF present in ./models (pre-download with download-models.sh
	# --weights). Uses the prebuilt llama.cpp image (CPU).
	set -euo pipefail
	cd "$(dirname "$0")/.."

	MODEL="${1:?usage: bench.sh /models/<file>.gguf (path is inside the container)}"
	THREADS_LIST="${THREADS_LIST:-1 2 4 8}"
	IMAGE="${LLAMA_IMAGE:-ghcr.io/ggml-org/llama.cpp:full}"
	OUT="scripts/bench-results"; mkdir -p "$OUT"
	STAMP="$(date +%Y%m%d-%H%M%S)"
	REPORT="$OUT/bench-$STAMP.md"

	echo "# llama-bench $(uname -n) $STAMP" \| tee "$REPORT"
	echo "model: $MODEL" \| tee -a "$REPORT"
	echo \| tee -a "$REPORT"

	for t in $THREADS_LIST; do
	echo "## threads=$t" \| tee -a "$REPORT"
	docker run --rm -v "$PWD/models:/models" "$IMAGE" \
	llama-bench -m "$MODEL" -t "$t" -p 512 -n 128 2>/dev/null \
	\| tee -a "$REPORT" \|\| echo " (run failed for t=$t)" \| tee -a "$REPORT"
	echo \| tee -a "$REPORT"
	done

	echo "Saved: $REPORT"