Instructions to use Sking0123/Simplifyr1.5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Sking0123/Simplifyr1.5b 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 Sking0123/Simplifyr1.5b:F16
# Run inference directly in the terminal:
llama cli -hf Sking0123/Simplifyr1.5b:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Sking0123/Simplifyr1.5b:F16
# Run inference directly in the terminal:
llama cli -hf Sking0123/Simplifyr1.5b:F16

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 Sking0123/Simplifyr1.5b:F16
# Run inference directly in the terminal:
./llama-cli -hf Sking0123/Simplifyr1.5b:F16

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 Sking0123/Simplifyr1.5b:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Sking0123/Simplifyr1.5b:F16

Use Docker

docker model run hf.co/Sking0123/Simplifyr1.5b:F16

LM Studio
Jan
Ollama
How to use Sking0123/Simplifyr1.5b with Ollama:
```
ollama run hf.co/Sking0123/Simplifyr1.5b:F16
```
Unsloth Desktop

How to use Sking0123/Simplifyr1.5b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Sking0123/Simplifyr1.5b:F16

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": "Sking0123/Simplifyr1.5b:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Sking0123/Simplifyr1.5b with Docker Model Runner:
```
docker model run hf.co/Sking0123/Simplifyr1.5b:F16
```

Lemonade

How to use Sking0123/Simplifyr1.5b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Sking0123/Simplifyr1.5b:F16

Run and chat with the model

lemonade run user.Simplifyr1.5b-F16

List all available models

lemonade list

Hermes Agent

How to use Sking0123/Simplifyr1.5b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Sking0123/Simplifyr1.5b:F16

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 Sking0123/Simplifyr1.5b:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Sking0123/Simplifyr1.5b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Sking0123/Simplifyr1.5b:F16

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 "Sking0123/Simplifyr1.5b:F16" \
  --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"

🛡️ Simplifyr-1.5B

Teach it your logs once — it names every field forever.

Qwen2.5-1.5B fine-tuned to map perimeter-device log fields to Simplifyr's semantic catalog (source.ip, network.action, rule.id, threat.level, …). It is the suggestion engine of Simplifyr (mentioning where the app repo lives if public, else drop this line) — a Universal Log Pre-processing Framework that turns heterogeneous firewall/server logs into standardized, analytics-ready events without losing the original.

🎯 What it does

Raw log in — structured field mapping out:

{
  "suggestions": [
    {"input_field": "srcip", "semantic_field": "source.ip", "confidence": 0.92},
    {"input_field": "ruleid", "semantic_field": "rule.id", "confidence": 0.92},
    {"input_field": "mystery_xyz", "semantic_field": "", "confidence": 0.0}
  ]
}

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GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

16-bit