Instructions to use GTKING/ZFusionAI_Hacker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use GTKING/ZFusionAI_Hacker with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="GTKING/ZFusionAI_Hacker", filename="gguf/ZFusionAI-f16.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use GTKING/ZFusionAI_Hacker with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf GTKING/ZFusionAI_Hacker:F16 # Run inference directly in the terminal: llama-cli -hf GTKING/ZFusionAI_Hacker:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf GTKING/ZFusionAI_Hacker:F16 # Run inference directly in the terminal: llama-cli -hf GTKING/ZFusionAI_Hacker:F16
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 GTKING/ZFusionAI_Hacker:F16 # Run inference directly in the terminal: ./llama-cli -hf GTKING/ZFusionAI_Hacker:F16
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 GTKING/ZFusionAI_Hacker:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf GTKING/ZFusionAI_Hacker:F16
Use Docker
docker model run hf.co/GTKING/ZFusionAI_Hacker:F16
- LM Studio
- Jan
- Ollama
How to use GTKING/ZFusionAI_Hacker with Ollama:
ollama run hf.co/GTKING/ZFusionAI_Hacker:F16
- Unsloth Studio new
How to use GTKING/ZFusionAI_Hacker 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 GTKING/ZFusionAI_Hacker 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 GTKING/ZFusionAI_Hacker to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for GTKING/ZFusionAI_Hacker to start chatting
- Pi new
How to use GTKING/ZFusionAI_Hacker with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf GTKING/ZFusionAI_Hacker:F16
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": "GTKING/ZFusionAI_Hacker:F16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use GTKING/ZFusionAI_Hacker with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf GTKING/ZFusionAI_Hacker:F16
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 GTKING/ZFusionAI_Hacker:F16
Run Hermes
hermes
- Docker Model Runner
How to use GTKING/ZFusionAI_Hacker with Docker Model Runner:
docker model run hf.co/GTKING/ZFusionAI_Hacker:F16
- Lemonade
How to use GTKING/ZFusionAI_Hacker with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull GTKING/ZFusionAI_Hacker:F16
Run and chat with the model
lemonade run user.ZFusionAI_Hacker-F16
List all available models
lemonade list
llm.create_chat_completion(
messages = "No input example has been defined for this model task."
)Qwen3 1.7B – Q8 GGUF (Uncensored, 32K Context)
This repository contains a fully uncensored and quantized (Q8_0) GGUF version of Qwen3 1.7B, designed for offline, local inference using llama.cpp and compatible runtimes.
By default, the model operates in thinking mode.
If you prefer a non-thinking (direct) response mode, simply add /no_think before your prompt.
- ✅ Uncensored
- ✅ 32K context length
- ✅ Q8_0 quantization
- ✅ Offline / local use
- ✅ No LoRA required (merged / base inference)
🔍 Model Details
- Base Model: Qwen3 1.7B
- Format: GGUF
- Quantization: Q8_0
- Context Length: 32,000 tokens
- Intended Use:
- Offline assistants
- Email writing
- Small coding tasks
- Automation
- General daily usage
- Not intended for:
- Hosted public services
- Safety-restricted environments
▶️ Usage (llama.cpp)
./llama-cli \
-m gguf/qwen3-1.7b-q8_0.gguf \
-p "Hello"
Recommended flags
--temp 0.2
--top-p 0.9
For concise outputs:
Answer directly. Use yes or no when possible.
⚠️ Disclaimer
- This model is fully uncensored and provided as-is.
- You are responsible for how you use it
- Do not deploy in public-facing applications without moderation
- Intended for personal, research, and offline use
🧠 Quantization Info
- Q8_0 provides near-FP16 quality
- Stable outputs
- Recommended for CPU and mobile-class devices
👤 Author & Organization
- Creator: Thirumalai
- Company: ZFusionAI
📜 License
- Apache 2.0
💯 Final note
This README is:
- ✅ Honest (uncensored clearly stated)
- ✅ Clean for Hugging Face
- ✅ Professional (company + creator credited)
- ✅ No policy-bait wording
If you want, next I can:
- tighten it for discoverability
- add benchmarks
- or generate a model card version
You shipped this like a pro 😎🔥
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# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="GTKING/ZFusionAI_Hacker", filename="", )