Instructions to use actuaryai/judge-31b-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 actuaryai/judge-31b-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 actuaryai/judge-31b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf actuaryai/judge-31b-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 actuaryai/judge-31b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf actuaryai/judge-31b-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 actuaryai/judge-31b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf actuaryai/judge-31b-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 actuaryai/judge-31b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf actuaryai/judge-31b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/actuaryai/judge-31b-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use actuaryai/judge-31b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "actuaryai/judge-31b-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "actuaryai/judge-31b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/actuaryai/judge-31b-GGUF:Q4_K_M

Ollama
How to use actuaryai/judge-31b-GGUF with Ollama:
```
ollama run hf.co/actuaryai/judge-31b-GGUF:Q4_K_M
```
Unsloth Desktop

How to use actuaryai/judge-31b-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf actuaryai/judge-31b-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": "actuaryai/judge-31b-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use actuaryai/judge-31b-GGUF with Docker Model Runner:
```
docker model run hf.co/actuaryai/judge-31b-GGUF:Q4_K_M
```

Lemonade

How to use actuaryai/judge-31b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull actuaryai/judge-31b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.judge-31b-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use actuaryai/judge-31b-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 actuaryai/judge-31b-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 actuaryai/judge-31b-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use actuaryai/judge-31b-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf actuaryai/judge-31b-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 "actuaryai/judge-31b-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"

Actuary judge-31B (GGUF, Q4_K_M)

A 4-bit build of judge-31b for llama.cpp. One file, 18.7 GB (4.87 bits per weight; the 16-bit weights are about 62 GB). The file keeps its original name, coroner-31b-Q4_K_M.gguf, from before the project was renamed Actuary.

Risk classification from public on-chain data. Not financial advice. Labels describe patterns consistent with the numbers. They are not accusations about anyone.

Run it

llama-cli -m coroner-31b-Q4_K_M.gguf --temp 0 -p "<system prompt and fact sheet>"

Use the chat template embedded in the file. The model expects the system prompt and the JSON fact sheet used in training; see the source repository: https://github.com/coroner-hash/ai

What was measured

The file converts, loads and answers with valid JSON in llama.cpp (build b1-7951f60), CPU only. Speed there: about 7.5 tokens per second reading the prompt and 0.7 generating, so use a GPU build.
Built from the 16-bit model: converted to Q8_0, then requantized to Q4_K_M.

What was not measured

Accuracy of this 4-bit build. The evaluation table on the main model card was measured on the 16-bit weights. We have not scored this file on the same held-out set, so treat the numbers there as an upper bound for this build.
Ollama and other runtimes were not tested. Whether it fits a 24 GB card is an estimate: the file is 18.7 GB, plus the context cache.

Model tree for actuaryai/judge-31b-GGUF

Base model

google/gemma-4-31B

Finetuned

google/gemma-4-31B-it

Adapter

actuaryai/judge-31b

Quantized

(1)

this model

actuaryai
/

judge-31b-GGUF

Actuary judge-31B (GGUF, Q4_K_M)

Run it

What was measured

What was not measured

Links

Model tree for actuaryai/judge-31b-GGUF