Instructions to use arunb74/Kev-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use arunb74/Kev-4B 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 arunb74/Kev-4B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf arunb74/Kev-4B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf arunb74/Kev-4B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf arunb74/Kev-4B: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 arunb74/Kev-4B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf arunb74/Kev-4B: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 arunb74/Kev-4B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf arunb74/Kev-4B:Q4_K_M

Use Docker

docker model run hf.co/arunb74/Kev-4B:Q4_K_M

LM Studio
Jan
Ollama
How to use arunb74/Kev-4B with Ollama:
```
ollama run hf.co/arunb74/Kev-4B:Q4_K_M
```
Unsloth Desktop

How to use arunb74/Kev-4B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf arunb74/Kev-4B: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": "arunb74/Kev-4B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use arunb74/Kev-4B with Docker Model Runner:
```
docker model run hf.co/arunb74/Kev-4B:Q4_K_M
```

Lemonade

How to use arunb74/Kev-4B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull arunb74/Kev-4B:Q4_K_M

Run and chat with the model

lemonade run user.Kev-4B-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use arunb74/Kev-4B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf arunb74/Kev-4B: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 arunb74/Kev-4B:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use arunb74/Kev-4B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf arunb74/Kev-4B: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 "arunb74/Kev-4B: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"

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Kev-4B (GGUF)

Credit: This is a quantized GGUF version of the original Kev-4B created by Jared Palmer. All credit for the model architecture, training, and capabilities goes to the original author.

Model Summary

Kev-4B is a highly efficient decision model. Unlike standard LLMs, it does not generate text. Instead, it reads a document (the state) and a set of typed questions, returning a calibrated probability distribution over the provided options in a single forward pass.

This repository contains the Q4_K_M GGUF quantized version of the model, making it incredibly lightweight and optimized for local CPU/GPU inference via llama.cpp or similar local inference engines.

Intended Use

It is intended for developers building AI agents that need to:

Classify, route, triage, or check documents.
Obtain strict mathematical probabilities that can be thresholded (e.g., to send uncertain cases to human review).
Serve the TypeSafe public System One API (POST /v1/systemone).

Model Details

Original Developer: Jared Palmer (jaredpalmer/kev)
Model Type: Decision model (causal language-model backbone run prefill-only, with a pointer head over the options)
Format: GGUF (Q4_K_M quantization)
Backbone: Qwen3.5-4B-Base (32 layers, frozen)
Context Length: States of up to 65,536 tokens, plus at least 8,192 tokens per question.
Languages: English
License: Apache-2.0

Downloads last month: 75

GGUF

Model size

4B params

Architecture

qwen35

Hardware compatibility

4-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support