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

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

Hermes Agent

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

Atomic Chat

gemma4-stack / .env.example

Neohosseinism

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

89bf59d 25 days ago

Raw

History Blame Contribute Delete

1.72 kB

	# =============================================================================
	# Gemma 4 stack configuration. Copy a profile over this file, e.g.:
	# cp profiles/dev-cpu.env .env
	# then edit HF_TOKEN. Every value below has a sensible default in compose.
	# =============================================================================

	# --- Hugging Face (required: Gemma 4 is gated) -------------------------------
	HF_TOKEN=

	# --- Hardware / inference knobs (rendered into llama-swap config) ------------
	NGL=0 # GPU layers to offload. 0 = pure CPU. 99 = all (GPU).
	THREADS=4 # physical cores (this box = 4). Not hyperthreads.
	CTX=8192 # context window per model

	# --- Default model shown in Open WebUI (must match a llama-swap config key) --
	DEFAULT_MODEL=gemma-e4b # gemma-e4b \| gemma-12b \| gemma-26b-a4b

	# --- RAG models (Persian-tuned) ----------------------------------------------
	TEI_EMBED_MODEL=BAAI/bge-m3 # Persian, FaMTEB top-2 (Hakim's HF repo is unpublished)
	TEI_EMBED_POOLING=cls # bge-m3 uses cls pooling
	TEI_RERANK_MODEL=BAAI/bge-reranker-v2-m3
	TEI_DTYPE=float16 # float16 -> lighter Candle backend on CPU (saves RAM)

	# --- Images (override for GPU via docker-compose.gpu.yml) --------------------
	LLAMASWAP_IMAGE=ghcr.io/mostlygeek/llama-swap:cpu
	TEI_IMAGE=ghcr.io/huggingface/text-embeddings-inference:cpu-1.9

	# --- Host ports ---------------------------------------------------------------
	WEBUI_PORT=3000
	LLAMASWAP_PORT=8080
	TEI_EMBED_PORT=8081
	TEI_RERANK_PORT=8082

	# --- Misc ---------------------------------------------------------------------
	OPENAI_API_KEY=sk-local
	TEI_API_KEY=x
	WEBUI_NAME=Gemma 4 Local