Instructions to use liodon-ai/VulnLLM-R-7B-imatrix-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 liodon-ai/VulnLLM-R-7B-imatrix-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 liodon-ai/VulnLLM-R-7B-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf liodon-ai/VulnLLM-R-7B-imatrix-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 liodon-ai/VulnLLM-R-7B-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf liodon-ai/VulnLLM-R-7B-imatrix-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf liodon-ai/VulnLLM-R-7B-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf liodon-ai/VulnLLM-R-7B-imatrix-GGUF:Q4_K_M

Use Docker

docker model run hf.co/liodon-ai/VulnLLM-R-7B-imatrix-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use liodon-ai/VulnLLM-R-7B-imatrix-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "liodon-ai/VulnLLM-R-7B-imatrix-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": "liodon-ai/VulnLLM-R-7B-imatrix-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/liodon-ai/VulnLLM-R-7B-imatrix-GGUF:Q4_K_M

Ollama
How to use liodon-ai/VulnLLM-R-7B-imatrix-GGUF with Ollama:
```
ollama run hf.co/liodon-ai/VulnLLM-R-7B-imatrix-GGUF:Q4_K_M
```
Unsloth Desktop

How to use liodon-ai/VulnLLM-R-7B-imatrix-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf liodon-ai/VulnLLM-R-7B-imatrix-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": "liodon-ai/VulnLLM-R-7B-imatrix-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use liodon-ai/VulnLLM-R-7B-imatrix-GGUF with Docker Model Runner:
```
docker model run hf.co/liodon-ai/VulnLLM-R-7B-imatrix-GGUF:Q4_K_M
```

Lemonade

How to use liodon-ai/VulnLLM-R-7B-imatrix-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull liodon-ai/VulnLLM-R-7B-imatrix-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.VulnLLM-R-7B-imatrix-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use liodon-ai/VulnLLM-R-7B-imatrix-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 liodon-ai/VulnLLM-R-7B-imatrix-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 liodon-ai/VulnLLM-R-7B-imatrix-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use liodon-ai/VulnLLM-R-7B-imatrix-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf liodon-ai/VulnLLM-R-7B-imatrix-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 "liodon-ai/VulnLLM-R-7B-imatrix-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"

VulnLLM-R-7B-imatrix-GGUF / README.md

PY-AI-Dev

Add iMatrix GGUF quantizations for VulnLLM-R-7B

727c178 verified 4 months ago

preview code

Raw History Blame Contribute Delete

1.94 kB

metadata

license: other
base_model: Virtue-AI-HUB/VulnLLM-R-7B
pipeline_tag: text-generation
tags:
  - gguf
  - local-llm
  - llama.cpp
  - lm-studio
  - quantized
  - imatrix
  - sub-4-bit
  - qwen2
  - base_model:Qwen/Qwen2.5-7B-Instruct

VulnLLM-R-7B — iMatrix GGUF

GGUF quantizations of Virtue-AI-HUB/VulnLLM-R-7B, published by Liodon AI.

Quick Start

llama.cpp

llama-cli -hf liodon-ai/VulnLLM-R-7B-imatrix-GGUF:Q4_K_M

Ollama

ollama run hf.co/liodon-ai/VulnLLM-R-7B-imatrix-GGUF:Q4_K_M

LM Studio / Jan — search liodon-ai/VulnLLM-R-7B-imatrix-GGUF and pick your quant.

Quants

Quant	Size	VRAM est.	Notes
`IQ2_M`	2.78 GB	~3 GB	2-bit, iMatrix — smallest usable
`IQ3_M`	3.57 GB	~4 GB	3-bit, iMatrix — great quality/size tradeoff
`IQ4_XS`	4.22 GB	~5 GB	4-bit extra-small, iMatrix
`Q4_K_M`	4.68 GB	~5 GB	4-bit, iMatrix-calibrated (recommended)
`Q5_K_M`	5.44 GB	~6 GB	5-bit, iMatrix-calibrated
`Q6_K`	6.25 GB	~7 GB	6-bit, iMatrix-calibrated, near-lossless
`Q8_0`	8.10 GB	~9 GB	8-bit, essentially lossless

What is iMatrix?

Standard quantization treats all weights equally. iMatrix runs 128 calibration chunks through the full-precision model to find which weights matter most, then allocates more precision where it counts. At Q2/Q3/Q4 this means noticeably better coherence and instruction-following — same file size, better output.

Calibration: 2M tokens of WikiText-103.

Also see plain (non-iMatrix) quants: liodon-ai/VulnLLM-R-7B-GGUF

Source

Model: Virtue-AI-HUB/VulnLLM-R-7B
License: other

Quantized by Liodon AI