Instructions to use vagrillo/kaizen-qwen3-4b-instruct-2507 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use vagrillo/kaizen-qwen3-4b-instruct-2507 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 vagrillo/kaizen-qwen3-4b-instruct-2507:Q4_K_M # Run inference directly in the terminal: llama cli -hf vagrillo/kaizen-qwen3-4b-instruct-2507:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf vagrillo/kaizen-qwen3-4b-instruct-2507:Q4_K_M # Run inference directly in the terminal: llama cli -hf vagrillo/kaizen-qwen3-4b-instruct-2507: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 vagrillo/kaizen-qwen3-4b-instruct-2507:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf vagrillo/kaizen-qwen3-4b-instruct-2507: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 vagrillo/kaizen-qwen3-4b-instruct-2507:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf vagrillo/kaizen-qwen3-4b-instruct-2507:Q4_K_M
Use Docker
docker model run hf.co/vagrillo/kaizen-qwen3-4b-instruct-2507:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use vagrillo/kaizen-qwen3-4b-instruct-2507 with Ollama:
ollama run hf.co/vagrillo/kaizen-qwen3-4b-instruct-2507:Q4_K_M
- Unsloth Studio
How to use vagrillo/kaizen-qwen3-4b-instruct-2507 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 vagrillo/kaizen-qwen3-4b-instruct-2507 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 vagrillo/kaizen-qwen3-4b-instruct-2507 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for vagrillo/kaizen-qwen3-4b-instruct-2507 to start chatting
- Pi
How to use vagrillo/kaizen-qwen3-4b-instruct-2507 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf vagrillo/kaizen-qwen3-4b-instruct-2507:Q4_K_M
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": "vagrillo/kaizen-qwen3-4b-instruct-2507:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use vagrillo/kaizen-qwen3-4b-instruct-2507 with Docker Model Runner:
docker model run hf.co/vagrillo/kaizen-qwen3-4b-instruct-2507:Q4_K_M
- Lemonade
How to use vagrillo/kaizen-qwen3-4b-instruct-2507 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull vagrillo/kaizen-qwen3-4b-instruct-2507:Q4_K_M
Run and chat with the model
lemonade run user.kaizen-qwen3-4b-instruct-2507-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use vagrillo/kaizen-qwen3-4b-instruct-2507 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf vagrillo/kaizen-qwen3-4b-instruct-2507: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 vagrillo/kaizen-qwen3-4b-instruct-2507:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use vagrillo/kaizen-qwen3-4b-instruct-2507 with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf vagrillo/kaizen-qwen3-4b-instruct-2507:Q4_K_M
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 "vagrillo/kaizen-qwen3-4b-instruct-2507:Q4_K_M" \ --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"
kaizen-qwen3-4b-instruct-2507 - GGUF
An experiment in artistic communication through artificial hallucination
The Kai Zen Project
This model represents a fine-tuning experiment inspired by the imaginary reality of Kai Zen stories, particularly from the narrative cycle "Journey to the End of the Night When We Burned Chrome."
Reference: https://kaizenology.wordpress.com/2020/06/27/viaggio-al-termine-della-notte-che-bruciammo-chrome/
Artistic Objective
The goal of this model is not conventional text generation, but the production of artificial hallucinations that inhabit the Kai Zen world. It represents an attempt to implement a new form of artistic communication that is neither written nor spoken, but "thought."
This is not a traditional novel, but rather the ideal artificial representation of the novel itself - a machine that dreams parallel worlds through language.
Model Specifications
- Architecture: Qwen2 4B Instruct
- Format: GGUF (CPU-optimized)
- Purpose: Generation of hallucinatory narratives and alternative realities
- Context: 8K tokens
Available Model Files
qwen3-4b-instruct-2507.Q8_0.gguf(highest quality)qwen3-4b-instruct-2507.Q4_K_M.gguf(balanced quality/size)
Usage
With llama.cpp:
# For text-only models:
llama-cli --hf repo_id/model_name -p "describe a Kai Zen hallucination"
# For multimodal models:
llama-mtmd-cli -m model_name.gguf --mmproj mmproj_file.gguf
With Ollama
A Modelfile is included for easy deployment with Ollama.
Recommended Prompts
For authentic experiences in the Kai Zen world, try prompts such as:
- "Generate a hallucination of the night terminal"
- "Describe an entity that inhabits burned Chrome"
- "Tell a thought from the edge of digital reality"
Disclaimer
This is an experimental model intended for artistic research and exploration of new narrative forms. The generated content is meant as digital art pieces and not as representations of reality.
"Not what is written, but what could be thought"
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