Instructions to use automationkasey/brp-merged-q8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use automationkasey/brp-merged-q8 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="automationkasey/brp-merged-q8",
	filename="brp-q8.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use automationkasey/brp-merged-q8 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 automationkasey/brp-merged-q8
# Run inference directly in the terminal:
llama cli -hf automationkasey/brp-merged-q8

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf automationkasey/brp-merged-q8
# Run inference directly in the terminal:
llama cli -hf automationkasey/brp-merged-q8

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 automationkasey/brp-merged-q8
# Run inference directly in the terminal:
./llama-cli -hf automationkasey/brp-merged-q8

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 automationkasey/brp-merged-q8
# Run inference directly in the terminal:
./build/bin/llama-cli -hf automationkasey/brp-merged-q8

Use Docker

docker model run hf.co/automationkasey/brp-merged-q8

LM Studio
Jan
Ollama
How to use automationkasey/brp-merged-q8 with Ollama:
```
ollama run hf.co/automationkasey/brp-merged-q8
```

Unsloth Studio

How to use automationkasey/brp-merged-q8 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for automationkasey/brp-merged-q8 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for automationkasey/brp-merged-q8 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for automationkasey/brp-merged-q8 to start chatting

How to use automationkasey/brp-merged-q8 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf automationkasey/brp-merged-q8

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "automationkasey/brp-merged-q8"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use automationkasey/brp-merged-q8 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf automationkasey/brp-merged-q8

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 automationkasey/brp-merged-q8

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use automationkasey/brp-merged-q8 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf automationkasey/brp-merged-q8

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 "automationkasey/brp-merged-q8" \
  --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"

Docker Model Runner
How to use automationkasey/brp-merged-q8 with Docker Model Runner:
```
docker model run hf.co/automationkasey/brp-merged-q8
```

Lemonade

How to use automationkasey/brp-merged-q8 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull automationkasey/brp-merged-q8

Run and chat with the model

lemonade run user.brp-merged-q8-{{QUANT_TAG}}

List all available models

lemonade list

BRP — Bill Russell Protocol Model

Fine-tuned from Qwen2.5-7B-Instruct on the BRP dataset (524 pairs).

Capabilities

BRP threat scoring and policy enforcement
BRP incident detection and response
BRP protocol flow and architecture understanding
BRP self-vulnerability assessment

Usage

ollama pull hf.co/automationkasey/brp-merged-q8

Downloads last month: 8

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Inference Providers NEW

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

automationkasey
/

brp-merged-q8

BRP — Bill Russell Protocol Model

Capabilities

Usage

Dataset used to train automationkasey/brp-merged-q8