Instructions to use alexm37/rune-text-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use alexm37/rune-text-v1 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 alexm37/rune-text-v1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf alexm37/rune-text-v1:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf alexm37/rune-text-v1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf alexm37/rune-text-v1: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 alexm37/rune-text-v1:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf alexm37/rune-text-v1: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 alexm37/rune-text-v1:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf alexm37/rune-text-v1:Q4_K_M

Use Docker

docker model run hf.co/alexm37/rune-text-v1:Q4_K_M

LM Studio
Jan
Ollama
How to use alexm37/rune-text-v1 with Ollama:
```
ollama run hf.co/alexm37/rune-text-v1:Q4_K_M
```
Unsloth Desktop

How to use alexm37/rune-text-v1 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf alexm37/rune-text-v1: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": "alexm37/rune-text-v1:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use alexm37/rune-text-v1 with Docker Model Runner:
```
docker model run hf.co/alexm37/rune-text-v1:Q4_K_M
```

Lemonade

How to use alexm37/rune-text-v1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull alexm37/rune-text-v1:Q4_K_M

Run and chat with the model

lemonade run user.rune-text-v1-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use alexm37/rune-text-v1 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf alexm37/rune-text-v1: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 alexm37/rune-text-v1:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use alexm37/rune-text-v1 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf alexm37/rune-text-v1: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 "alexm37/rune-text-v1: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"

Rune Text v1

Rune Text v1 is the local ranking model used by Rune Keyboard Smart Typing. This artifact is the reproducible Q4_K_M conversion of Qwen/Qwen3-0.6B-Base revision da87bfb608c14b7cf20ba1ce41287e8de496c0cd, produced with llama.cpp revision 36b10154383b60eb15baac2c7a40d2a5f784faa7.

Artifact identity

File: rune-text-v1-0.1.0-q4_k_m.gguf
Bytes: 396704416
SHA-256: 7a97111c917e19117207428971fa1c2583f2d9c2a07a6fda5b6f198b707dd9c4
GGUF: v3, qwen3, Q4_K_M

Rune Keyboard verifies the exact size and SHA-256 before activation and executes inference locally in a private Android process. Typed text is not uploaded by the keyboard.

Smart Typing evidence

The frozen production-candidate holdout at the user-selected 95% point-precision profile produced:

Language	Automatic changes	Correct	Precision	False changes / 1000
English	304	293	96.38%	0
Russian	359	342	95.26%	1
Spanish	408	396	97.06%	0

Original remained available in every candidate set. These measurements use the bounded Rune scorer and frozen product policy. The complete physical Fold latency, thermal, cancellation and process-death qualification is still pending, so this repository is marked experimental rather than a final Rune Keyboard release.

Intended use

The model scores a bounded list of spelling or punctuation continuations. It is not exposed as an unrestricted text generator. Rune Keyboard retains protected-token checks, bounded candidate search, confidence thresholds, immediate Undo, and local deterministic capitalization for supported proper names.

License

Apache License 2.0. See LICENSE.

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GGUF

Model size

0.6B params

Architecture

qwen3

Hardware compatibility

4-bit

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