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 / README.md

crh225

Plumb-4B v5 GGUF (Q8_0, Q4_K_M)

71c1c57 13 days ago

preview code

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	---
	license: apache-2.0
	base_model: crh225/plumb-4b
	language: [en]
	tags: [gguf, llama.cpp, ollama, decision-model, calibration]
	---

	# Plumb-4B GGUF

	[Plumb-4B](https://huggingface.co/crh225/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:

	```bash
	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

	```bash
	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
	```

	```python
	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"]})
	```

	Converted with llama.cpp (`convert_hf_to_gguf.py --no-mtp`, then `llama-quantize`). Apache-2.0; see the
	main model's card for credits.