Instructions to use UtkarshSinghShrinet/swarmhunter-3b-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 UtkarshSinghShrinet/swarmhunter-3b-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 UtkarshSinghShrinet/swarmhunter-3b-gguf:Q8_0
# Run inference directly in the terminal:
llama cli -hf UtkarshSinghShrinet/swarmhunter-3b-gguf:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf UtkarshSinghShrinet/swarmhunter-3b-gguf:Q8_0
# Run inference directly in the terminal:
llama cli -hf UtkarshSinghShrinet/swarmhunter-3b-gguf:Q8_0

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 UtkarshSinghShrinet/swarmhunter-3b-gguf:Q8_0
# Run inference directly in the terminal:
./build/bin/llama cli -hf UtkarshSinghShrinet/swarmhunter-3b-gguf:Q8_0

Use Docker

docker model run hf.co/UtkarshSinghShrinet/swarmhunter-3b-gguf:Q8_0

Llama new
LM Studio
Jan

vLLM

How to use UtkarshSinghShrinet/swarmhunter-3b-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "UtkarshSinghShrinet/swarmhunter-3b-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": "UtkarshSinghShrinet/swarmhunter-3b-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/UtkarshSinghShrinet/swarmhunter-3b-gguf:Q8_0

Ollama
How to use UtkarshSinghShrinet/swarmhunter-3b-gguf with Ollama:
```
ollama run hf.co/UtkarshSinghShrinet/swarmhunter-3b-gguf:Q8_0
```
Unsloth Desktop

How to use UtkarshSinghShrinet/swarmhunter-3b-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf UtkarshSinghShrinet/swarmhunter-3b-gguf:Q8_0

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": "UtkarshSinghShrinet/swarmhunter-3b-gguf:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use UtkarshSinghShrinet/swarmhunter-3b-gguf with Docker Model Runner:
```
docker model run hf.co/UtkarshSinghShrinet/swarmhunter-3b-gguf:Q8_0
```

Lemonade

How to use UtkarshSinghShrinet/swarmhunter-3b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull UtkarshSinghShrinet/swarmhunter-3b-gguf:Q8_0

Run and chat with the model

lemonade run user.swarmhunter-3b-gguf-Q8_0

List all available models

lemonade list

Hermes Agent

How to use UtkarshSinghShrinet/swarmhunter-3b-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 UtkarshSinghShrinet/swarmhunter-3b-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 UtkarshSinghShrinet/swarmhunter-3b-gguf:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use UtkarshSinghShrinet/swarmhunter-3b-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf UtkarshSinghShrinet/swarmhunter-3b-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 "UtkarshSinghShrinet/swarmhunter-3b-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"

🛡️ SwarmHunter 3B (GGUF Quantized)

Quantized GGUF build (Q8_0) of SwarmHunter 3B Mega SFT, an autonomous cybersecurity reasoning model created and trained by Utkarsh Singh Shrinet across 2,280,000 security and exploit engineering samples on top of Qwen/Qwen2.5-Coder-3B-Instruct.

⚡ Direct Ollama Run (No Manual Setup Needed!)

You can run this model directly in Ollama using the Hugging Face hub integration:

ollama run hf.co/UtkarshSinghShrinet/swarmhunter-3b-gguf:latest

Or download the .gguf file directly and use with llama.cpp, LM Studio, or custom runtimes.

🔬 Model Details

Creator & Developer: Utkarsh Singh Shrinet
Base Architecture: Qwen2.5-Coder-3B-Instruct (3.09B params)
Quantization: Q8_0 High-Precision Quantization (~3.06 GB)
Training Samples: 2,280,000 SFT instruction pairs (CVE, CWE, ExploitDB, Pentest, Code Auditing)
Training Strategy: Sequential Cumulative LoRA Chaining (Rank 16, Alpha 32, 46 Parts)
Final SFT Loss: 0.9617 (Part 46 loss: 0.7187, Mean Token Accuracy: 80.62%)