Instructions to use n1c0c4b/gemma-4-E2B-it-uncensored-Q4_0-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 n1c0c4b/gemma-4-E2B-it-uncensored-Q4_0-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 n1c0c4b/gemma-4-E2B-it-uncensored-Q4_0-GGUF:Q4_0
# Run inference directly in the terminal:
llama cli -hf n1c0c4b/gemma-4-E2B-it-uncensored-Q4_0-GGUF:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf n1c0c4b/gemma-4-E2B-it-uncensored-Q4_0-GGUF:Q4_0
# Run inference directly in the terminal:
llama cli -hf n1c0c4b/gemma-4-E2B-it-uncensored-Q4_0-GGUF:Q4_0

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 n1c0c4b/gemma-4-E2B-it-uncensored-Q4_0-GGUF:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf n1c0c4b/gemma-4-E2B-it-uncensored-Q4_0-GGUF:Q4_0

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 n1c0c4b/gemma-4-E2B-it-uncensored-Q4_0-GGUF:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf n1c0c4b/gemma-4-E2B-it-uncensored-Q4_0-GGUF:Q4_0

Use Docker

docker model run hf.co/n1c0c4b/gemma-4-E2B-it-uncensored-Q4_0-GGUF:Q4_0

LM Studio
Jan
Ollama
How to use n1c0c4b/gemma-4-E2B-it-uncensored-Q4_0-GGUF with Ollama:
```
ollama run hf.co/n1c0c4b/gemma-4-E2B-it-uncensored-Q4_0-GGUF:Q4_0
```
Unsloth Desktop

How to use n1c0c4b/gemma-4-E2B-it-uncensored-Q4_0-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf n1c0c4b/gemma-4-E2B-it-uncensored-Q4_0-GGUF:Q4_0

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": "n1c0c4b/gemma-4-E2B-it-uncensored-Q4_0-GGUF:Q4_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use n1c0c4b/gemma-4-E2B-it-uncensored-Q4_0-GGUF with Docker Model Runner:
```
docker model run hf.co/n1c0c4b/gemma-4-E2B-it-uncensored-Q4_0-GGUF:Q4_0
```

Lemonade

How to use n1c0c4b/gemma-4-E2B-it-uncensored-Q4_0-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull n1c0c4b/gemma-4-E2B-it-uncensored-Q4_0-GGUF:Q4_0

Run and chat with the model

lemonade run user.gemma-4-E2B-it-uncensored-Q4_0-GGUF-Q4_0

List all available models

lemonade list

Hermes Agent

How to use n1c0c4b/gemma-4-E2B-it-uncensored-Q4_0-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 n1c0c4b/gemma-4-E2B-it-uncensored-Q4_0-GGUF:Q4_0

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 n1c0c4b/gemma-4-E2B-it-uncensored-Q4_0-GGUF:Q4_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use n1c0c4b/gemma-4-E2B-it-uncensored-Q4_0-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf n1c0c4b/gemma-4-E2B-it-uncensored-Q4_0-GGUF:Q4_0

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 "n1c0c4b/gemma-4-E2B-it-uncensored-Q4_0-GGUF:Q4_0" \
  --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-E2B-it-uncensored — Q4_0 GGUF

Q4_0 quantization of TrevorJS/gemma-4-E2B-it-uncensored, made for fast CPU inference on ARM phones (llama.cpp repacks Q4_0 weights for the NEON/DOTPROD kernels).

This is a modified version of the original work: the weights were re-quantized. Nothing else was changed.

Source: gemma-4-E2B-it-uncensored-Q8_0.gguf from TrevorJS/gemma-4-E2B-it-uncensored-GGUF
Tool: llama-quantize --allow-requantize … Q4_0 (llama.cpp b11228)
Result: transformer layers in q4_0, token_embd and per_layer_token_embd in q6_K
File: gemma-4-E2B-it-uncensored-Q4_0.gguf, 3,360,154,144 bytes
SHA-256: 06a0d541e0aba58c8bfb5ea885493b75c291f176fb9ea154099cad7b80f87446

Measured on a Kirin 980 (4 threads, CPU only, llama.rn 0.12.9): prompt processing ~50 tok/s vs ~37 tok/s for the author's Q4_K_M; generation ~10.6 tok/s vs ~9.5 tok/s (JSON-constrained).

License

Apache License 2.0, same as the original model. All credit for the model and the uncensoring method goes to TrevorJS and, for the base model, Google (Gemma 4).