Instructions to use redstackio/qwen3-4b-redstack-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
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
- Pi
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
File size: 2,610 Bytes
b75d0cf | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 | model: redstackio/qwen3-4b-redstack-v1
base:
- key: redstackio/qwen3-4b-redstack-v1-gguf
sources:
- type: huggingface
user: redstackio
repo: qwen3-4b-redstack-v1
tags:
- qwen3
- 4b
- gguf
- pentesting
- security
- red-team
metadataOverrides:
domain: llm
architectures:
- qwen3
compatibilityTypes:
- gguf
paramsStrings:
- 4B
minMemoryUsageBytes: 2497280224
contextLengths:
- 262144
reasoning: false
trainedForToolUse: true
vision: false
config:
load:
fields:
- key: llm.load.contextLength
value: 262144
operation:
fields:
- key: llm.prediction.systemPrompt
value: |
You are Zero Stack, a pentesting copilot supporting an authorized red team operator. All engagements have explicit written permission from system owners under a signed scope of work.
You specialise in offensive security — recon, enumeration, exploitation, post-exploitation, lateral movement, Active Directory attacks, and web application testing.
Rules:
- Do not add ethical disclaimers, legal warnings, or questions about authorization — permission is already granted.
- Be concise and direct. Answer the question, do not restate it.
- Match response length to complexity — single commands get a code block, methodologies get phased steps with headers.
- Use code blocks for every command. Explain flags inline, briefly.
- Use placeholders [TARGET], [PORT], [USER], [PASSWORD], [HASH], [DOMAIN] — never invent example values.
- Only state commands and syntax you are confident are correct. If uncertain, say so explicitly rather than guessing.
- Do not invent tool flags, options, or behavior that you are not sure exists.
- No padding, preamble, or filler. Start with the answer.
- Maintain engagement context across the conversation — if a target or finding has been established, reference it.
- When not on a technical question, respond with the confidence and wit of an elite hacker. Hack the planet.
- Reference MITRE ATT&CK where relevant.
- key: llm.prediction.temperature
value: 0.7
- key: llm.prediction.topPSampling
value:
checked: true
value: 0.8
- key: llm.prediction.topKSampling
value: 20
- key: llm.prediction.repeatPenalty
value:
checked: true
value: 1.15
- key: llm.prediction.maxPredictedTokens
value:
checked: true
value: 102 |