Instructions to use Euraika/VulnLLM-R-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Euraika/VulnLLM-R-7B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Euraika/VulnLLM-R-7B-GGUF",
	filename="VulnLLM-R-7B-Q8_0.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Euraika/VulnLLM-R-7B-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 Euraika/VulnLLM-R-7B-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf Euraika/VulnLLM-R-7B-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Euraika/VulnLLM-R-7B-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf Euraika/VulnLLM-R-7B-GGUF:Q8_0

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 Euraika/VulnLLM-R-7B-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf Euraika/VulnLLM-R-7B-GGUF:Q8_0

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 Euraika/VulnLLM-R-7B-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Euraika/VulnLLM-R-7B-GGUF:Q8_0

Use Docker

docker model run hf.co/Euraika/VulnLLM-R-7B-GGUF:Q8_0

LM Studio
Jan

vLLM

How to use Euraika/VulnLLM-R-7B-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Euraika/VulnLLM-R-7B-GGUF:Q8_0

Ollama
How to use Euraika/VulnLLM-R-7B-GGUF with Ollama:
```
ollama run hf.co/Euraika/VulnLLM-R-7B-GGUF:Q8_0
```

Unsloth Studio

How to use Euraika/VulnLLM-R-7B-GGUF 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 Euraika/VulnLLM-R-7B-GGUF 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 Euraika/VulnLLM-R-7B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Euraika/VulnLLM-R-7B-GGUF to start chatting

How to use Euraika/VulnLLM-R-7B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Euraika/VulnLLM-R-7B-GGUF:Q8_0

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": "Euraika/VulnLLM-R-7B-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Euraika/VulnLLM-R-7B-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 Euraika/VulnLLM-R-7B-GGUF:Q8_0

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 Euraika/VulnLLM-R-7B-GGUF:Q8_0

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Euraika/VulnLLM-R-7B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Euraika/VulnLLM-R-7B-GGUF:Q8_0

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 "Euraika/VulnLLM-R-7B-GGUF:Q8_0" \
  --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 Euraika/VulnLLM-R-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/Euraika/VulnLLM-R-7B-GGUF:Q8_0
```

Lemonade

How to use Euraika/VulnLLM-R-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Euraika/VulnLLM-R-7B-GGUF:Q8_0

Run and chat with the model

lemonade run user.VulnLLM-R-7B-GGUF-Q8_0

List all available models

lemonade list

VulnLLM-R-7B — GGUF

GGUF conversions of Virtue-AI-HUB/VulnLLM-R-7B, a reasoning model for software vulnerability detection, fine-tuned from Qwen/Qwen2.5-7B-Instruct. Paper: arXiv:2512.07533.

These files run the model locally with llama.cpp, Ollama, LM Studio, and any GGUF runtime.

Files

File	Quant	Size	Notes
`VulnLLM-R-7B-f16.gguf`	F16	~15 GB	Full-precision GGUF; best quality, largest.
`VulnLLM-R-7B-Q8_0.gguf`	Q8_0	~8 GB	8-bit; near-lossless, recommended for most use.

Model specs

Property	Value
Architecture	`qwen2` (Qwen2.5-7B-Instruct base)
Parameters	~7.6 B
Max context length	32,768 tokens (32K) — native
Layers	28
Embedding dim	3584
Attention	GQA, 28 Q heads / 4 KV heads
Chat template	ChatML (embedded in GGUF metadata)

Context notes: 32K is the native maximum (max_position_embeddings / qwen2.context_length). This fine-tune ships no rope_scaling, so there is no supported YaRN path beyond 32K — pushing past it degrades quality. Set your runtime window (num_ctx / -c) as high as 32768; higher windows cost more KV-cache memory (roughly: 8K ≈ 4 GB, 16K ≈ 7 GB, 32K ≈ 15 GB on top of the ~8 GB Q8_0 weights).

⚠️ This is a reasoning model — use the system prompt

VulnLLM-R generates a step-by-step chain-of-thought (inside a </think>-delimited block) before its final answer. If you prompt it with a bare question and no framing, it can drift out-of-distribution and produce noisy output. Always give it the analysis framing below — ideally as the system message so it triggers reliably.

Recommended system prompt:

You are an advanced vulnerability detection model. Analyze the following code
step-by-step to determine whether it contains a security vulnerability. Reason
about data flow, control flow, and security context, then give a final answer
that names the vulnerability, its CWE, and how to fix it. If the code is safe, say so.

Then send the code snippet as the user message.

Suggested sampling: temperature 0.3, top_p 0.8 (deterministic-leaning analysis).

Usage

Ollama

# Pull directly from this repo (Q8_0):
ollama pull hf.co/Euraika/VulnLLM-R-7B-GGUF:Q8_0

# Recommended: bake in the system prompt, sampling, and full 32K context via a Modelfile:
cat > Modelfile <<'MF'
FROM hf.co/Euraika/VulnLLM-R-7B-GGUF:Q8_0
SYSTEM """You are an advanced vulnerability detection model. Analyze the following code step-by-step to determine whether it contains a security vulnerability. Reason about data flow, control flow, and security context, then give a final answer that names the vulnerability, its CWE, and how to fix it. If the code is safe, say so."""
PARAMETER temperature 0.3
PARAMETER top_p 0.8
PARAMETER num_ctx 32768
MF
ollama create vulnllm-r-7b -f Modelfile
ollama run vulnllm-r-7b "<paste code here>"

llama.cpp

llama-cli -hf Euraika/VulnLLM-R-7B-GGUF:Q8_0 --temp 0.3 --top-p 0.8 -c 32768 \
  -sys "You are an advanced vulnerability detection model. Analyze the code step-by-step, reason about data/control flow and security context, then give a final answer with the vulnerability, its CWE, and a fix." \
  -p "<paste code here>"

Chat format (ChatML)

<|im_start|>system
{system}<|im_end|>
<|im_start|>user
{code}<|im_end|>
<|im_start|>assistant

Conversion

Converted from the original safetensors weights to GGUF and quantized to Q8_0. The F16 GGUF is the intermediate full-precision conversion; Q8_0 is quantized from it.

License & attribution

Released under Apache-2.0, inherited from the base model Virtue-AI-HUB/VulnLLM-R-7B (derived from Qwen2.5-7B-Instruct, Apache-2.0). All credit for the model belongs to the original authors; this repository only provides GGUF conversions. Please cite the paper and follow the upstream terms.

@article{nie2025vulnllmr,
  title={VulnLLM-R: Specialized Reasoning LLM with Agent Scaffold for Vulnerability Detection},
  author={Nie, Yuzhou and Li, Hongwei and Guo, Chengquan and Jiang, Ruizhe and Wang, Zhun and Li, Bo and Song, Dawn and Guo, Wenbo},
  journal={arXiv preprint arXiv:2512.07533},
  year={2025}
}