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

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use GTKING/ZFusionAI_Hacker 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 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 serve -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

Llama new
LM Studio
Jan
Ollama
How to use GTKING/ZFusionAI_Hacker with Ollama:
```
ollama run hf.co/GTKING/ZFusionAI_Hacker:F16
```
Unsloth Desktop

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 serve -hf GTKING/ZFusionAI_Hacker:F16

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": "GTKING/ZFusionAI_Hacker:F16"
        }
      ]
    }
  }
}

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

Hermes Agent

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 serve -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

Atomic Chat

OpenClaw

How to use GTKING/ZFusionAI_Hacker with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf GTKING/ZFusionAI_Hacker:F16

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 "GTKING/ZFusionAI_Hacker:F16" \
  --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"

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: 191

Safetensors

Model size

2B params

Tensor type

BF16

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Quantizations

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