Instructions to use tomvaillant/gemma4-e4b-journalist with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use tomvaillant/gemma4-e4b-journalist 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 tomvaillant/gemma4-e4b-journalist:BF16
# Run inference directly in the terminal:
llama cli -hf tomvaillant/gemma4-e4b-journalist:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf tomvaillant/gemma4-e4b-journalist:BF16
# Run inference directly in the terminal:
llama cli -hf tomvaillant/gemma4-e4b-journalist:BF16

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 tomvaillant/gemma4-e4b-journalist:BF16
# Run inference directly in the terminal:
./llama-cli -hf tomvaillant/gemma4-e4b-journalist:BF16

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 tomvaillant/gemma4-e4b-journalist:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tomvaillant/gemma4-e4b-journalist:BF16

Use Docker

docker model run hf.co/tomvaillant/gemma4-e4b-journalist:BF16

LM Studio
Jan
Ollama
How to use tomvaillant/gemma4-e4b-journalist with Ollama:
```
ollama run hf.co/tomvaillant/gemma4-e4b-journalist:BF16
```

Unsloth Studio

How to use tomvaillant/gemma4-e4b-journalist 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 tomvaillant/gemma4-e4b-journalist 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 tomvaillant/gemma4-e4b-journalist to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for tomvaillant/gemma4-e4b-journalist to start chatting

How to use tomvaillant/gemma4-e4b-journalist with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tomvaillant/gemma4-e4b-journalist:BF16

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": "tomvaillant/gemma4-e4b-journalist:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use tomvaillant/gemma4-e4b-journalist with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tomvaillant/gemma4-e4b-journalist:BF16

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 "tomvaillant/gemma4-e4b-journalist:BF16" \
  --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 tomvaillant/gemma4-e4b-journalist with Docker Model Runner:
```
docker model run hf.co/tomvaillant/gemma4-e4b-journalist:BF16
```

Lemonade

How to use tomvaillant/gemma4-e4b-journalist with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tomvaillant/gemma4-e4b-journalist:BF16

Run and chat with the model

lemonade run user.gemma4-e4b-journalist-BF16

List all available models

lemonade list

Hermes Agent

How to use tomvaillant/gemma4-e4b-journalist with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tomvaillant/gemma4-e4b-journalist:BF16

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 tomvaillant/gemma4-e4b-journalist:BF16

Run Hermes

hermes

Atomic Chat

gemma4-e4b-journalist

A compact investigative journalism model fine-tuned on google/gemma-4-E4B-it (8B params), designed for browser-sized deployment via WebGPU. Same training data and domain coverage as gemma4-31b-journalist.

Built by Buried Signals for edge/browser inference in OSINT Navigator.

Training

Method: QLoRA (4-bit NF4 + LoRA r=16, alpha=32) via Oumi
Data: 700 instruction/response pairs across 10 categories — see training dataset
Epochs: 1
Hardware: A10G (HF Jobs)

Sources

Same training corpus as the 31B variant. Covers OSINT tool selection, verification methodology, financial investigation (Follow the Money), digital security, media ethics, and investigative storytelling.

Key sources include the OSINT Navigator Tool Database (7,524 tools), Bellingcat guides, GIJN manuals, UNESCO/Al Jazeera/CiFAR handbooks, SPJ ethics, RCFP legal resources, and Buried Signals investigation skill repositories.

Full attribution: SOURCES.md

GGUF

This model was converted to GGUF format using Unsloth.

Example usage:

Text only: llama-cli -hf tomvaillant/gemma4-e4b-journalist --jinja
Multimodal: llama-mtmd-cli -hf tomvaillant/gemma4-e4b-journalist --jinja

Available files

gemma-4-E4B-it.Q4_K_M.gguf
gemma-4-E4B-it.BF16-mmproj.gguf

This was trained 2x faster with Unsloth

Downloads last month: 107

GGUF

Model size

8B params

Architecture

gemma4

Hardware compatibility

4-bit

Inference Providers NEW

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