Instructions to use crh225/plumb-4b-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 crh225/plumb-4b-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 crh225/plumb-4b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf crh225/plumb-4b-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf crh225/plumb-4b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf crh225/plumb-4b-GGUF: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 crh225/plumb-4b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf crh225/plumb-4b-GGUF: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 crh225/plumb-4b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf crh225/plumb-4b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/crh225/plumb-4b-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use crh225/plumb-4b-GGUF with Ollama:
```
ollama run hf.co/crh225/plumb-4b-GGUF:Q4_K_M
```
Unsloth Desktop

How to use crh225/plumb-4b-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf crh225/plumb-4b-GGUF: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": "crh225/plumb-4b-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use crh225/plumb-4b-GGUF with Docker Model Runner:
```
docker model run hf.co/crh225/plumb-4b-GGUF:Q4_K_M
```

Lemonade

How to use crh225/plumb-4b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull crh225/plumb-4b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.plumb-4b-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use crh225/plumb-4b-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 crh225/plumb-4b-GGUF: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 crh225/plumb-4b-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use crh225/plumb-4b-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf crh225/plumb-4b-GGUF: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 "crh225/plumb-4b-GGUF: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"

Plumb-4B GGUF

Plumb-4B for llama.cpp and Ollama, on almost any GPU or a CPU.

File	Quantisation	Size
`plumb-4b-v5-Q8_0.gguf`	8-bit, closest to the full model	~4.5 GB
`plumb-4b-v5-Q4_K_M.gguf`	4-bit	2.7 GB

Plumb-4B is a decision model, not a chat model: it answers with one option letter, and its value is in the probabilities of those letters, softened by its calibration temperature T = 2.07.

With Ollama

Modelfile in this repo holds the decision prompt as the template, so an ordinary chat request carrying the question as JSON (evidence, criterion, lettered options) renders the right prompt:

ollama create plumb-4b -f Modelfile      # next to the downloaded .gguf

Ask for one token with logprobs and top_logprobs, send reasoning_effort: "none", read the option letters' log-probabilities, divide by 2.07 and renormalise.

With llama.cpp

llama-server -m plumb-4b-v5-Q8_0.gguf -c 8192 -ngl 99
pip install --no-deps "jevk5 @ git+https://github.com/allebee/jevk5@v0.2.1"   # standard library only

from jevk5 import JevK5GGUF

model = JevK5GGUF(temperature=2.07)   # llama-server on :8080
model.decide("Order #7120 shows delivered to No. 17; the customer lives at No. 71.",
             {"type": "choice", "instructions": "What happened to the parcel?",
              "criteria": ["delivered", "misdelivered", "unknown"]})