Text Generation
GGUF
English
uncensored
lower-refusal
coding
reasoning
tool-use
cybersecurity
local-ai
ollama
conversational
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
- llama.cpp
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
- Pi
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"
File size: 12,699 Bytes
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import json
import logging
import shutil
import subprocess
import threading
from pathlib import Path
from typing import Any, Dict, Optional, Tuple
from flask import Flask, jsonify, render_template, request
logger = logging.getLogger("modInteractive.admin")
BASE_DIR = Path(__file__).resolve().parents[1]
app = Flask(
__name__,
template_folder=str(Path(__file__).resolve().parent / "templates"),
static_folder=str(Path(__file__).resolve().parent / "static"),
)
_config: Optional[Any] = None
_config_data: Dict[str, Any] = {}
_config_path: Path = BASE_DIR / "config.json"
def init(config: Any, config_path: str) -> None:
global _config, _config_data, _config_path
_config = config
_config_path = Path(config_path).expanduser().resolve()
try:
if hasattr(config, "reload"):
config.reload()
elif hasattr(config, "load"):
config.load()
data = getattr(config, "data", {})
_config_data = dict(data) if isinstance(data, dict) else {}
except Exception:
logger.exception("Admin config initialization failed")
_config_data = _read_config_file()
@app.route("/")
def index() -> str:
return render_template("index.html")
@app.route("/favicon.ico")
def favicon() -> tuple[str, int]:
return "", 204
@app.route("/api/config", methods=["GET"])
def get_config() -> Any:
data = _read_config_file()
if not data:
return jsonify({"error": "Config not found or empty"}), 404
return jsonify(data)
@app.route("/api/config/update", methods=["POST"])
def update_config() -> Any:
global _config_data
data = request.get_json(silent=True)
if not isinstance(data, dict):
return jsonify({"error": "JSON object required"}), 400
try:
if _config is not None and hasattr(_config, "update"):
_config.update(data, save=True)
_config_data = dict(_config.data)
else:
current = _read_config_file()
merged = _deep_merge(current, data)
_write_config_file(merged)
_config_data = merged
return jsonify(
{
"status": "ok",
"message": "Configuration updated",
"config": _config_data,
}
)
except Exception as exc:
logger.exception("Config update failed")
return jsonify({"error": str(exc)}), 500
@app.route("/api/reload-config", methods=["POST"])
def reload_config() -> Any:
global _config_data
try:
if _config is not None and hasattr(_config, "reload"):
_config.reload()
_config_data = dict(_config.data)
else:
_config_data = _read_config_file()
return jsonify(
{
"status": "ok",
"message": "Configuration reloaded",
"config": _config_data,
}
)
except Exception as exc:
logger.exception("Config reload failed")
return jsonify({"error": str(exc)}), 500
@app.route("/api/status", methods=["GET"])
def get_status() -> Any:
data = _current_config()
trigger_source = str(_nested_get(data, "trigger.source", "camera")).lower().strip()
status: Dict[str, Any] = {
"admin": "running",
"project_root": str(BASE_DIR),
"config_path": str(_config_path),
"trigger_source": trigger_source,
"opencv_available": False,
"opencv_version": None,
"camera_available": False,
"camera_resolution": None,
"pir_available": False,
"pir_gpio_pin": _safe_int(_nested_get(data, "pir.gpio_pin", 17), 17, 0, 27),
"pir_state": None,
"video_exists": False,
"video_path": None,
"mpv_available": shutil.which("mpv") is not None,
"mpv_path": shutil.which("mpv"),
}
if trigger_source == "camera":
_fill_camera_status(status, data)
elif trigger_source == "pir":
_fill_pir_status(status, data)
video_path = _resolve_video_path(str(_nested_get(data, "video.path", "videos/selamlama.mp4")))
status["video_path"] = str(video_path)
status["video_exists"] = video_path.is_file()
if video_path.is_file():
status["video_size_mb"] = round(video_path.stat().st_size / (1024 * 1024), 2)
return jsonify(status)
@app.route("/api/test-video", methods=["POST"])
def test_video() -> Any:
data = _current_config()
mpv_path = shutil.which(str(_nested_get(data, "video.player", "mpv")))
if not mpv_path:
return jsonify({"error": "mpv not installed. Install: sudo apt install mpv"}), 500
requested_video = None
body = request.get_json(silent=True)
if isinstance(body, dict):
requested_video = body.get("path")
video_value = str(requested_video or _nested_get(data, "video.path", "videos/selamlama.mp4"))
video_path = _resolve_video_path(video_value)
allowed, reason = _is_allowed_video_path(video_path)
if not allowed:
return jsonify({"error": reason}), 403
if not video_path.is_file():
return jsonify({"error": f"Video not found: {video_path}"}), 404
fullscreen = bool(_nested_get(data, "video.fullscreen", True))
volume = _safe_int(_nested_get(data, "video.volume", 90), 90, 0, 100)
command = [
mpv_path,
"--no-terminal",
"--really-quiet",
"--keep-open=no",
"--osd-level=0",
f"--volume={volume}",
]
if fullscreen:
command.extend(["--fs", "--no-border", "--ontop", "--cursor-autohide=always"])
command.append(str(video_path))
try:
subprocess.Popen(
command,
stdin=subprocess.DEVNULL,
stdout=subprocess.DEVNULL,
stderr=subprocess.DEVNULL,
shell=False,
start_new_session=True,
)
return jsonify(
{
"status": "ok",
"message": "Video playback started",
"video": str(video_path),
}
)
except Exception as exc:
logger.exception("Test video failed")
return jsonify({"error": str(exc)}), 500
@app.route("/api/logs", methods=["GET"])
def get_logs() -> Any:
limit = _safe_int(request.args.get("limit", 100), 100, 1, 1000)
log_file = BASE_DIR / "logs" / "modinteractive.log"
if not log_file.exists():
return jsonify({"logs": []})
try:
lines = log_file.read_text(encoding="utf-8", errors="replace").splitlines()
return jsonify({"logs": lines[-limit:]})
except Exception as exc:
logger.exception("Reading logs failed")
return jsonify({"error": str(exc)}), 500
def run_server(host: str = "0.0.0.0", port: int = 8080) -> None:
try:
logger.info("Admin panel starting on http://%s:%d", host, port)
app.run(
host=host,
port=int(port),
debug=False,
use_reloader=False,
threaded=True,
)
except Exception:
logger.exception("Admin panel failed to start")
def start_admin_thread(config: Any, config_path: str) -> threading.Thread:
init(config, config_path)
host = str(config.get("admin.host", "0.0.0.0"))
port = _safe_int(config.get("admin.port", 8080), 8080, 1, 65535)
thread = threading.Thread(
target=run_server,
args=(host, port),
daemon=True,
name="modinteractive-admin-server",
)
thread.start()
logger.info("Admin panel thread started on port %d", port)
return thread
def _fill_camera_status(status: Dict[str, Any], data: Dict[str, Any]) -> None:
try:
import cv2
status["opencv_available"] = True
status["opencv_version"] = cv2.__version__
camera_index = _normalize_camera_index(
_nested_get(data, "camera.index", 0)
)
camera_width = _safe_int(_nested_get(data, "camera.width", 640), 640, 1)
camera_height = _safe_int(_nested_get(data, "camera.height", 480), 480, 1)
camera_fps = _safe_int(_nested_get(data, "camera.fps", 15), 15, 1)
camera_backend = str(_nested_get(data, "camera.backend", "v4l2")).lower()
if camera_backend == "v4l2" and hasattr(cv2, "CAP_V4L2"):
cap = cv2.VideoCapture(camera_index, cv2.CAP_V4L2)
else:
cap = cv2.VideoCapture(camera_index)
try:
if cap.isOpened():
cap.set(cv2.CAP_PROP_FRAME_WIDTH, camera_width)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, camera_height)
cap.set(cv2.CAP_PROP_FPS, camera_fps)
ok, frame = cap.read()
if ok and frame is not None:
status["camera_available"] = True
status["camera_resolution"] = f"{frame.shape[1]}x{frame.shape[0]}"
else:
status["camera_available"] = True
status["camera_resolution"] = "opened but no frame"
finally:
cap.release()
except Exception as exc:
status["camera_error"] = str(exc)
def _fill_pir_status(status: Dict[str, Any], data: Dict[str, Any]) -> None:
"""Report PIR status without trying to claim GPIO already owned by the app.
The admin panel runs inside the same application process. When PIR mode is active,
the main loop already owns the GPIO line, so opening the same BCM pin again from
the status endpoint may produce "GPIO busy". For the dashboard we report the
configured pin and that the GPIO is controlled by the running app. The standalone
health check and tools/test_pir.py still perform real GPIO read tests.
"""
status["pir_available"] = True
status["pir_state"] = "managed_by_application"
status["pir_note"] = "GPIO is controlled by the running modInteractive process"
def _current_config() -> Dict[str, Any]:
if _config is not None:
try:
data = getattr(_config, "data", None)
if isinstance(data, dict):
return dict(data)
except Exception:
logger.debug("Could not read live config object", exc_info=True)
return _read_config_file()
def _read_config_file() -> Dict[str, Any]:
try:
if not _config_path.exists():
return {}
data = json.loads(_config_path.read_text(encoding="utf-8"))
if isinstance(data, dict):
return data
return {}
except Exception:
logger.exception("Could not read config file")
return {}
def _write_config_file(data: Dict[str, Any]) -> None:
_config_path.parent.mkdir(parents=True, exist_ok=True)
_config_path.write_text(
json.dumps(data, indent=2, ensure_ascii=False) + "\n",
encoding="utf-8",
)
def _deep_merge(base: Dict[str, Any], override: Dict[str, Any]) -> Dict[str, Any]:
result = dict(base)
for key, value in override.items():
if isinstance(result.get(key), dict) and isinstance(value, dict):
result[key] = _deep_merge(result[key], value)
else:
result[key] = value
return result
def _nested_get(data: Dict[str, Any], key_path: str, default: Any = None) -> Any:
value: Any = data
for key in key_path.split("."):
if isinstance(value, dict) and key in value:
value = value[key]
else:
return default
return value
def _resolve_video_path(value: str) -> Path:
path = Path(value).expanduser()
if not path.is_absolute():
path = BASE_DIR / path
return path.resolve()
def _is_allowed_video_path(path: Path) -> Tuple[bool, str]:
videos_dir = (BASE_DIR / "videos").resolve()
try:
path.relative_to(videos_dir)
return True, ""
except ValueError:
return False, f"Only files under videos directory are allowed: {videos_dir}"
def _normalize_camera_index(value: Any) -> Any:
if isinstance(value, int):
return value
if isinstance(value, str):
stripped = value.strip()
if stripped.isdigit():
return int(stripped)
return stripped
try:
return int(value)
except (TypeError, ValueError):
return 0
def _safe_int(
value: Any,
default: int,
minimum: Optional[int] = None,
maximum: Optional[int] = None,
) -> int:
try:
number = int(value)
except (TypeError, ValueError):
number = default
if minimum is not None and number < minimum:
number = minimum
if maximum is not None and number > maximum:
number = maximum
return number
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