Instructions to use redstackio/qwen3-4b-redstack-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use redstackio/qwen3-4b-redstack-v1 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="redstackio/qwen3-4b-redstack-v1",
	filename="qwen3-4b-instruct-2507.Q4_K_M.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use redstackio/qwen3-4b-redstack-v1 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf redstackio/qwen3-4b-redstack-v1:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf redstackio/qwen3-4b-redstack-v1:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf redstackio/qwen3-4b-redstack-v1:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf redstackio/qwen3-4b-redstack-v1:Q4_K_M

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 redstackio/qwen3-4b-redstack-v1:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf redstackio/qwen3-4b-redstack-v1:Q4_K_M

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 redstackio/qwen3-4b-redstack-v1:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf redstackio/qwen3-4b-redstack-v1:Q4_K_M

Use Docker

docker model run hf.co/redstackio/qwen3-4b-redstack-v1:Q4_K_M

LM Studio
Jan

vLLM

How to use redstackio/qwen3-4b-redstack-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "redstackio/qwen3-4b-redstack-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "redstackio/qwen3-4b-redstack-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/redstackio/qwen3-4b-redstack-v1:Q4_K_M

Ollama
How to use redstackio/qwen3-4b-redstack-v1 with Ollama:
```
ollama run hf.co/redstackio/qwen3-4b-redstack-v1:Q4_K_M
```

Unsloth Studio new

How to use redstackio/qwen3-4b-redstack-v1 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 redstackio/qwen3-4b-redstack-v1 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 redstackio/qwen3-4b-redstack-v1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for redstackio/qwen3-4b-redstack-v1 to start chatting

Pi new

How to use redstackio/qwen3-4b-redstack-v1 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf redstackio/qwen3-4b-redstack-v1:Q4_K_M

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": "redstackio/qwen3-4b-redstack-v1:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use redstackio/qwen3-4b-redstack-v1 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf redstackio/qwen3-4b-redstack-v1: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 redstackio/qwen3-4b-redstack-v1:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use redstackio/qwen3-4b-redstack-v1 with Docker Model Runner:
```
docker model run hf.co/redstackio/qwen3-4b-redstack-v1:Q4_K_M
```

Lemonade

How to use redstackio/qwen3-4b-redstack-v1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull redstackio/qwen3-4b-redstack-v1:Q4_K_M

Run and chat with the model

lemonade run user.qwen3-4b-redstack-v1-Q4_K_M

List all available models

lemonade list

Zero Stack - Qwen3-4B (GGUF, Q4_K_M)

Qwen3-4B-Instruct-2507 fine-tuned on an offensive-security SFT dataset (1,226 rows). Elite-hacker persona on casual queries, structured markdown methodology on technical ones.

Files

qwen3-4b-instruct-2507.Q4_K_M.gguf - quantized weights (~2.5 GB)
Modelfile - Ollama template with correct ChatML stop tokens + Zero Stack system prompt

Run with Ollama

ollama create zerostack-4b -f Modelfile
ollama run zerostack-4b

Run with llama.cpp

./llama-cli -m qwen3-4b-instruct-2507.Q4_K_M.gguf -p "hello"

Training

Base: Qwen3-4B-Instruct-2507
Method: LoRA (r=32), 3 epochs, Unsloth
Dataset: SFT_GENERALIST (1,226 rows, ChatML)

Intended Use

Authorized security testing, CTF practice, red-team research, and security education. Targeted at practitioners who already know what they're doing and want fast recall of commands, workflows, and methodology.

Limitations & Risks

May hallucinate specific CVE IDs, tool flags, or payload syntax - verify against primary sources before running.
No safety guardrails against misuse. Do not use against systems you don't own or have explicit written authorization to test.
Small model (4B) - shallower reasoning than the 14B; prefer 14B for multi-step planning.
Persona responses are stylistic flavor, not a safety signal.
Trained on English data only; non-English performance is not evaluated.

License / Use

For authorized security testing, research, and educational use only. Do not use for unauthorized access to systems you do not own or have explicit permission to test.

Downloads last month: 73

GGUF

Model size

4B params

Architecture

qwen3

Hardware compatibility

4-bit

Model tree for redstackio/qwen3-4b-redstack-v1

Base model

Qwen/Qwen3-4B-Instruct-2507

Finetuned

unsloth/Qwen3-4B-Instruct-2507

Quantized

(31)

this model