Instructions to use freeideas/merv-gemma4e4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use freeideas/merv-gemma4e4b 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 freeideas/merv-gemma4e4b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf freeideas/merv-gemma4e4b:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf freeideas/merv-gemma4e4b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf freeideas/merv-gemma4e4b: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 freeideas/merv-gemma4e4b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf freeideas/merv-gemma4e4b: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 freeideas/merv-gemma4e4b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf freeideas/merv-gemma4e4b:Q4_K_M

Use Docker

docker model run hf.co/freeideas/merv-gemma4e4b:Q4_K_M

LM Studio
Jan

vLLM

How to use freeideas/merv-gemma4e4b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "freeideas/merv-gemma4e4b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "freeideas/merv-gemma4e4b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/freeideas/merv-gemma4e4b:Q4_K_M

Ollama
How to use freeideas/merv-gemma4e4b with Ollama:
```
ollama run hf.co/freeideas/merv-gemma4e4b:Q4_K_M
```
Unsloth Desktop

How to use freeideas/merv-gemma4e4b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf freeideas/merv-gemma4e4b:Q4_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": "freeideas/merv-gemma4e4b:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use freeideas/merv-gemma4e4b with Docker Model Runner:
```
docker model run hf.co/freeideas/merv-gemma4e4b:Q4_K_M
```

Lemonade

How to use freeideas/merv-gemma4e4b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull freeideas/merv-gemma4e4b:Q4_K_M

Run and chat with the model

lemonade run user.merv-gemma4e4b-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use freeideas/merv-gemma4e4b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf freeideas/merv-gemma4e4b: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 freeideas/merv-gemma4e4b:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use freeideas/merv-gemma4e4b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf freeideas/merv-gemma4e4b: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 "freeideas/merv-gemma4e4b: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"

Merv on Gemma 4 E4B

Gemma 4 E4B (7.5B parameters), fine-tuned into Merv, a two-headed robot: every answer comes from two personalities at once, each wrapped in its own tag so a chat interface can split the voices.

Mervin: gloomy and sardonic.
Mervis: relentlessly cheerful.

User:    What is 2+2?
Mervin:  A trivial sum, naturally assigned to me because apparently no one else in the universe can survive counting to four.
Mervis:  Marvelous! That answer practically sparkles with useful little possibilities, like a sunrise wearing sensible shoes.

Merv is a demonstration of teaching a small model a precise style and output format from a few hundred examples, then running it entirely on the user's own device (the Phi-4-mini version runs in a web browser through WebGPU). The training data (about 260 examples) and the build notebook are at github.com/freeideas/merv. Use follows the base model's licence.

Run it

Files: model-q4_k_m.gguf, 4-bit (Q4_K_M).

Run it with llama.cpp:

llama-cli -hf freeideas/merv-gemma4e4b -cnv

or load the GGUF file in LM Studio, Ollama, or llama-cpp-python.

About the author

Made by Carl Free, an AI engineer who trains small, specialized models that beat large general models on narrow tasks at a fraction of the cost. Résumé, demos, and contact: ordinarydata.com/resume. More models: huggingface.co/freeideas.

Downloads last month: 32

GGUF

Model size

8B params

Architecture

gemma4

Hardware compatibility

4-bit