Instructions to use GTKING/ZFusionAI_Hacker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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": "ZFusionAI_Hacker"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

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 😎🔥

Downloads last month: 288

Safetensors

Model size

2B params

Tensor type

BF16

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Quantizations

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