Instructions to use redstackio/qwen3-14b-redstack-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use redstackio/qwen3-14b-redstack-v1 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="redstackio/qwen3-14b-redstack-v1",
	filename="qwen3-14b.Q5_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/qwen3-14b-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/qwen3-14b-redstack-v1:Q5_K_M
# Run inference directly in the terminal:
llama-cli -hf redstackio/qwen3-14b-redstack-v1:Q5_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf redstackio/qwen3-14b-redstack-v1:Q5_K_M
# Run inference directly in the terminal:
llama-cli -hf redstackio/qwen3-14b-redstack-v1:Q5_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/qwen3-14b-redstack-v1:Q5_K_M
# Run inference directly in the terminal:
./llama-cli -hf redstackio/qwen3-14b-redstack-v1:Q5_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/qwen3-14b-redstack-v1:Q5_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf redstackio/qwen3-14b-redstack-v1:Q5_K_M

Use Docker

docker model run hf.co/redstackio/qwen3-14b-redstack-v1:Q5_K_M

LM Studio
Jan

vLLM

How to use redstackio/qwen3-14b-redstack-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "redstackio/qwen3-14b-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/qwen3-14b-redstack-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/redstackio/qwen3-14b-redstack-v1:Q5_K_M

Ollama
How to use redstackio/qwen3-14b-redstack-v1 with Ollama:
```
ollama run hf.co/redstackio/qwen3-14b-redstack-v1:Q5_K_M
```

Unsloth Studio new

How to use redstackio/qwen3-14b-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/qwen3-14b-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/qwen3-14b-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/qwen3-14b-redstack-v1 to start chatting

Pi new

How to use redstackio/qwen3-14b-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/qwen3-14b-redstack-v1:Q5_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": "qwen3-14b-redstack-v1"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use redstackio/qwen3-14b-redstack-v1 with Docker Model Runner:
```
docker model run hf.co/redstackio/qwen3-14b-redstack-v1:Q5_K_M
```

Lemonade

How to use redstackio/qwen3-14b-redstack-v1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull redstackio/qwen3-14b-redstack-v1:Q5_K_M

Run and chat with the model

lemonade run user.qwen3-14b-redstack-v1-Q5_K_M

List all available models

lemonade list

qwen3-14b-redstack-v1 / README.md

LSB127

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97d0cfa verified 22 days ago

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metadata

license: other
pipeline_tag: text-generation
library_name: gguf
language:
  - en
base_model: Qwen/Qwen3-14B
base_model_relation: quantized
tags:
  - gguf
  - qwen3
  - pentesting
  - security
  - lora
  - sft

Zero Stack - Qwen3-14B (GGUF, Q5_K_M)

Qwen3-14B fine-tuned on an offensive-security SFT dataset (1,226 rows). Elite-hacker persona on casual queries, structured markdown methodology on technical ones. Thinking mode enabled by default (Qwen3-14B base behavior).

Files

qwen3-14b.Q5_K_M.gguf - quantized weights (~9.8 GB)
Modelfile - Ollama template with correct ChatML stop tokens + Zero Stack system prompt

Run with Ollama

ollama create zerostack-14b -f Modelfile
ollama run zerostack-14b

Run with llama.cpp

./llama-cli -m qwen3-14b.Q5_K_M.gguf -p "hello"

Training

Base: Qwen3-14B
Method: LoRA (r=32), 3 epochs, Unsloth
Max sequence length: 2560
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 structured methodology and command recall.

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.
Thinking mode is on by default - responses may be slower and include reasoning traces. Disable in Modelfile if you want faster, terser output.
Trained on English data only; non-English performance is not evaluated.
16 GB VRAM note: GGUF export uses CPU offloading to avoid LoRA merge corruption. If you retrain/re-export, verify maximum_memory_usage=0.5 in export_gguf.py.

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.