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 / Modelfile

crh225

Plumb-4B v5 GGUF (Q8_0, Q4_K_M)

71c1c57 13 days ago

Raw History Blame Contribute Delete

896 Bytes

	# Plumb-4B (v5, 4B, Q4_K_M), converted from crh225/plumb-4b with llama.cpp.
	#
	# A decision model, not a chat model, continued from JevK5 v0.2, so it uses
	# JevK5's exact prompt: an ordinary chat request carrying only the question's
	# JSON (evidence, criterion, lettered options) renders the right prompt inside
	# Ollama, the same way the JevK5 Modelfile does.
	#
	# Callers read the answer letter's log-probabilities from one generated token
	# and divide by Plumb-4B's calibration temperature T = 2.07.
	FROM ./plumb-4b-v5-Q4_K_M.gguf
	SYSTEM """Apply the supplied criterion to the supplied evidence. Choose exactly one listed option. Respond with only its uppercase letter, with no explanation or reasoning."""
	TEMPLATE """<\|im_start\|>system
	{{ .System }}<\|im_end\|>
	<\|im_start\|>user
	{{ .Prompt }}<\|im_end\|>
	<\|im_start\|>assistant
	<think>

	</think>

	"""
	PARAMETER temperature 0
	PARAMETER num_ctx 4096