Instructions to use chlebajz/pepa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use chlebajz/pepa 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 chlebajz/pepa:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf chlebajz/pepa:Q4_K_M

Install from WinGet (Windows)

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

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf chlebajz/pepa:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf chlebajz/pepa:Q4_K_M

Use Docker

docker model run hf.co/chlebajz/pepa:Q4_K_M

Llama new
LM Studio
Jan
Ollama
How to use chlebajz/pepa with Ollama:
```
ollama run hf.co/chlebajz/pepa:Q4_K_M
```
Unsloth Desktop

How to use chlebajz/pepa with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf chlebajz/pepa: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": "chlebajz/pepa:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use chlebajz/pepa with Docker Model Runner:
```
docker model run hf.co/chlebajz/pepa:Q4_K_M
```

Lemonade

How to use chlebajz/pepa with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull chlebajz/pepa:Q4_K_M

Run and chat with the model

lemonade run user.pepa-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use chlebajz/pepa with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf chlebajz/pepa: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 chlebajz/pepa:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use chlebajz/pepa with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf chlebajz/pepa: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 "chlebajz/pepa: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"

PEPA: modely

Modely pro PEPA (Prohlížečový Expert na Podvody s AI), rozšíření prohlížeče, které upozorní na český podvod v e-mailu, chatu nebo na webu ve chvíli, kdy ho čtete. Modely běží přímo v počítači uživatele.

Kód, instalátor a dokumentace: https://github.com/chlebja3/pepa. Instalátor si tyto soubory stáhne sám a ověří je podle install/MODELS.sha256 z repozitáře.

Cesta	Co to je	Základní model	Licence
`models/screener/scr-e5-fp/`	rychlé síto: vybere texty, které stojí za bližší kontrolu	multilingual-e5-small	MIT
`models/verifier/qwen-pair-q4_k_m.gguf`	ověřovač: rozhodne, jestli jde o podvod, a česky vysvětlí proč (Q4_K_M pro llama.cpp)	Qwen3.5-2B	Apache-2.0
`models/ocr/ces.traineddata`	česká OCR pro text v obrázcích (beze změny z Tesseract tessdata)	–	Apache-2.0

Skripty a hyperparametry trénování jsou ve složce training/ v repozitáři.

PEPA vznikl pro AI výzvu pro Česko 2026, v oblasti Prevence podvodů.

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GGUF

Model size

2B params

Architecture

qwen35

Hardware compatibility

4-bit

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