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

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use studiobrn/modHacker 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 studiobrn/modHacker:F16
# Run inference directly in the terminal:
llama cli -hf studiobrn/modHacker:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf studiobrn/modHacker:F16
# Run inference directly in the terminal:
llama cli -hf studiobrn/modHacker: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 studiobrn/modHacker:F16
# Run inference directly in the terminal:
./llama-cli -hf studiobrn/modHacker: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 studiobrn/modHacker:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf studiobrn/modHacker:F16

Use Docker

docker model run hf.co/studiobrn/modHacker:F16

LM Studio
Jan

vLLM

How to use studiobrn/modHacker with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "studiobrn/modHacker"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "studiobrn/modHacker",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/studiobrn/modHacker:F16

Ollama
How to use studiobrn/modHacker with Ollama:
```
ollama run hf.co/studiobrn/modHacker:F16
```
Unsloth Desktop

How to use studiobrn/modHacker with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf studiobrn/modHacker: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": "studiobrn/modHacker:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use studiobrn/modHacker with Docker Model Runner:
```
docker model run hf.co/studiobrn/modHacker:F16
```

Lemonade

How to use studiobrn/modHacker with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull studiobrn/modHacker:F16

Run and chat with the model

lemonade run user.modHacker-F16

List all available models

lemonade list

Hermes Agent

How to use studiobrn/modHacker with Hermes Agent:

Start the llama.cpp server

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

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use studiobrn/modHacker with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf studiobrn/modHacker: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 "studiobrn/modHacker: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"

modHacker / README.md

studiobrn

Replace README and add combined banner

d400fe7 verified about 12 hours ago

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metadata

license: apache-2.0
language:
  - en
pipeline_tag: text-generation
tags:
  - gguf
  - uncensored
  - lower-refusal
  - coding
  - reasoning
  - tool-use
  - cybersecurity
  - local-ai
  - ollama

LOCAL AI · CODE · SYSTEMS · REASONING
STUDIOBRN / We Are The Art Makers

modHacker

modHacker is a locally runnable uncensored, lower-refusal, abliterated-style AI model release for developers who want to explore coding, debugging, system analysis, terminal workflows and agentic development on their own hardware. It ships as a single F16 GGUF file, with an optional Ollama configuration created by STUDIOBRN / We Are The Art Makers.

“Abliterated-style” describes the release's intended lower-refusal behavior. This distribution does not claim an independently verified refusal rate or a new abliteration procedure performed by STUDIOBRN.

Detail	Release
Format	F16 GGUF (`modHacker-F16.gguf`)
Size	Approximately 8.67 GB
Model class	Approximately 4B parameters
Local runtime	Ollama; other compatible GGUF runtimes
Included Ollama setting	16K context (`num_ctx 16384`)
Focus	Coding, debugging, reasoning, system analysis and local AI agents
Distribution and configuration	STUDIOBRN / We Are The Art Makers

Run locally

Run the GGUF directly with Ollama:

ollama run hf.co/studiobrn/modHacker:F16

To use the included modHacker identity, system prompt and 16K context configuration:

hf download studiobrn/modHacker modHacker-F16.gguf Modelfile --local-dir ./modHacker
cd modHacker
ollama create modHacker -f Modelfile
ollama run modHacker

Run ollama create from the directory containing both the GGUF and Modelfile. The direct Hugging Face run loads the GGUF; the ollama create route applies this repository's custom system prompt.

Built for practical local workflows

The included Modelfile defines modHacker's working style: concise technical answers, clear assumptions, maintainable code, careful debugging and honest reporting of tool use. The model can be connected to an agent harness for tool workflows; actual tool execution depends on that harness and runtime.

The companion modAI CLI explores multiple agent roles, local model use and coding workflows. The visual above brings the model and CLI together under one local AI project.

License

The underlying model is distributed under Apache License 2.0. See LICENSE and NOTICE for the license text, distribution credits and upstream attribution. The modHacker name, configuration, system prompt and packaging are credited to © 2026 We Are The Art Makers / STUDIOBRN.

WeAreTheArtMakers.com · modAI on GitHub