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 Settings

llama.cpp

How to use redstackio/qwen3-4b-redstack-v1 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 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 serve -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

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

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 serve -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 serve -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

Atomic Chat new
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

qwen3-4b-redstack-v1

File size: 1,853 Bytes

c51de2e
 
fc51e59
 
 
 
c51de2e
fc51e59
c51de2e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
fc51e59
 
c51de2e
fc51e59

---
license: other
pipeline_tag: text-generation
library_name: gguf
language:
  - en
base_model: unsloth/Qwen3-4B-Instruct-2507
base_model_relation: quantized
tags:
  - gguf
  - qwen3
  - pentesting
  - security
  - lora
  - sft
---

# 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
```bash
ollama create zerostack-4b -f Modelfile
ollama run zerostack-4b
```

## Run with llama.cpp
```bash
./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.