llamastudio 1.0.0

FastAPI backend & desktop launcher for running local models via llama.cpp

🦙 LLamaStudio

A desktop chat interface and local server manager for llama.cpp, crafted with FastAPI + HTMX for ultra-lightweight, zero-framework execution.

LLamaStudio is a self-contained local workspace that manages model lifecycles, features a smart VRAM estimator, scans local folders, and lets you search and download models directly from the Hugging Face Hub.

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📸 Screenshots & Showcase

1. Main Chat Dashboard

A Pop!_OS-harmonized dark interface with streaming, collapsible markdown reasoning (thinking) processes, and real-time agentic tool execution logs.

2. GGUF Model Browser & Settings

A dynamic local model explorer that scans your directories and lets you adjust context length, GPU offload layers, CPU threads, flash attention, and KV cache quantizations on the fly.

3. Hugging Face Discover Hub

Browse the entire Hugging Face GGUF catalog. Features a Smart VRAM Offload Estimator calibrated to your hardware, and a floating background download progress card with live speed (MB/s), ETA, and cancel controls.

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✨ Key Features

• ⚡ Zero Node Modules: Built with HTMX, Tailwind CSS (via CDN), and Vanilla JS. It is incredibly fast, responsive, and has a memory footprint of just a few megabytes.
• 🧭 Hugging Face Discover Tab: Search the public Hugging Face Hub for GGUF models directly inside the app, view readmes, select quantizations, and download files in the background.
• 🚀 Smart VRAM Estimator: calulated specifically for your hardware (fits fully on RTX 5090 32GB VRAM, partial offload warning, or heavy CPU fallback warning).
• 📂 Automatic Model Scanning: Scans standard directories (like ~/.lmstudio/models) automatically on startup or via a one-click rescan button.
• 🪐 Process Lifecycle Manager: The underlying llama-server process only spins up when you explicitly load a model, releasing all system resources and GPU VRAM instantly when you click "Eject".
• 🔧 Configurable Workspace Sandboxing: Supports sandboxed agentic tool use (file read/write, commands, etc.) with real-time logs in the UI. Sandboxing boundaries can be easily broadened or bypassed entirely via a .env file or environment variables to grant the model system access.
• 🖥️ XDG-Compliant Persistence: Conversations and model settings profiles are stored outside the codebase directory in standard ~/.config/llamastudio/ with automated backward-compatible migrations!
• 📦 Full Linux & macOS Portability: Server binaries and model directories are resolved dynamically on startup.

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🛠️ Installation & Setup

LLamaStudio is compatible with Linux and macOS out-of-the-box. Choose your OS and python virtual environment preference below.

🐧 1. Linux Installation

Prerequisites

1. Python 3.10+ (Recommended: Python 3.13)
2. llama.cpp built from source (or pre-compiled binary):
   • By default, the app dynamically looks for the llama-server binary globally on your system PATH or locally inside your home directory at ~/llama.cpp/build/bin/llama-server.

Environment Setup

Option A: Install from PyPI

pip install llamastudio

Option B: Conda / Miniconda from source

# 1. Clone the repository
git clone https://github.com/gnulnx/LlamaStudio.git
cd LlamaStudio

# 2. Create and activate a conda environment
conda create -n llamastudio python=3.13 -y
conda activate llamastudio

# 3. Install LlamaStudio and its dependencies
pip install -e .

Option C: Python Virtualenv (venv) from source

# 1. Clone the repository
git clone https://github.com/gnulnx/LlamaStudio.git
cd LlamaStudio

# 2. Create and activate a python venv environment
python3 -m venv .venv
source .venv/bin/activate

# 3. Install LlamaStudio and its dependencies
pip install -e .

🖥️ Linux Desktop Launcher Integration (Optional)

To integrate LLamaStudio directly into your Linux Application launcher menu (e.g., GNOME / Pop!_OS):

# 1. Copy the desktop file to your local applications directory
cp llamastudio.desktop ~/.local/share/applications/

# 2. Copy the custom SVG icon to your local icons directory
mkdir -p ~/.local/share/icons/hicolor/128x128/apps/
cp llamastudio.svg ~/.local/share/icons/hicolor/128x128/apps/

# 3. Update your desktop database and icon cache
update-desktop-database ~/.local/share/applications/
gtk-update-icon-cache -f -t ~/.local/share/icons

Note: If you are using a virtualenv, edit the executable path inside ~/.local/share/applications/llamastudio.desktop to point to your specific .venv/bin/python interpreter.

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🍏 2. macOS Installation

Prerequisites

1. Python 3.10+
2. llama.cpp installed globally via Homebrew (highly recommended for macOS):

   brew install llama.cpp
   

   (This automatically places the llama-server binary globally on your system PATH, which LLamaStudio will auto-detect immediately!)

Environment Setup

Option A: Install from PyPI

pip install llamastudio

Option B: Conda / Miniconda from source

# 1. Clone the repository
git clone https://github.com/gnulnx/LlamaStudio.git
cd LlamaStudio

# 2. Create and activate environment
conda create -n llamastudio python=3.13 -y
conda activate llamastudio

# 3. Install LlamaStudio and its dependencies
pip install -e .

Option C: Python Virtualenv (venv) from source

# 1. Clone the repository
git clone https://github.com/gnulnx/LlamaStudio.git
cd LlamaStudio

# 2. Create and activate venv
python3 -m venv .venv
source .venv/bin/activate

# 3. Install LlamaStudio and its dependencies
pip install -e .

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🪟 3. Windows Installation

Note: Native Windows execution is currently untested. However, you can run LLamaStudio on Windows seamlessly via WSL2 (Windows Subsystem for Linux) by following the standard Linux Installation guide above.

Pull requests extending native Windows support (e.g., resolving .exe binaries) are highly welcome!

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🚀 Running the Application

Option A: Via Unified CLI (lls - Recommended)

You can link and install LlamaStudio's CLI utility locally to control the desktop app and server seamlessly:

# Start the desktop application server and open browser UI
lls reload

Option B: Via App Launcher Command

After installing from PyPI or source, run:

llamastudio

Option C: Via Direct Startup Script

When working from a source checkout, activate your virtual environment and run the startup script:

# Activate your env (Conda)
conda activate llamastudio
# OR (Venv)
source .venv/bin/activate

# Start the application
python start.py

This will launch the FastAPI backend on http://127.0.0.1:8765 and automatically open your default browser to the chat dashboard.

Via Application Menu (Linux)

Search for LLamaStudio in your desktop search bar (press Super, type "Llama") and click to launch!

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🛠️ Unified Command-Line Interface (lls)

LlamaStudio features a CLI built using rich-click for visual dashboards and operational efficiency.

CLI Subcommands Reference

Command	Usage	Description
reload	lls reload	Gracefully starts or restarts the desktop FastAPI application backend.
status	lls status	Visual dashboard of FastAPI backend status, loaded model parameters, and GPU memory (VRAM).
ls	lls ls	Prints an elegant table of all GGUF models scanned across local directories.
load	lls load [MODEL]	Boots the server with a GGUF model. If MODEL is omitted, prompts you with an interactive menu.
eject	lls eject	Gracefully unloads the active model to free GPU and CPU RAM.
oneshot	lls oneshot "prompt"	Streams thinking traces, text, and executes agentic tools directly in your terminal.

For example, to boot a model interactively:

$ lls load
Available Scanned Models:
  1. Qwen3.6-35B-A3B-UD-Q5_K_M (25.2 GB)
  2. gemma-4-26B-A4B-it-Q8_0 (25.0 GB)
  3. DeepSeek-R1-Distill-Qwen-32B-Q5_K_M (21.7 GB)

Select a model number to load: 3
Loading model 'DeepSeek-R1-Distill-Qwen-32B-Q5_K_M'...

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⚙️ Configuration & Customization

The application runs fully out-of-the-box with no manual configuration. However, you can customize default settings inside app/config.py or by using a local .env file:

• LLAMA_SERVER_BIN: The absolute path to your llama-server binary (automatically resolved on PATH/home).
• MODEL_DIRS: List of local directories to scan for GGUF model files (defaults to ~/.lmstudio/models).
• APP_PORT: FastAPI web server port (defaults to 8765).

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🛡️ Workspace Sandboxing & Embodiment

By default, LlamaStudio restricts agent tools (like reading, writing, and listing files) to the repository directory to prevent accidental path traversals.

If you want to grant your model more control (e.g., to act as a system embodiment or write to external directories), you can customize these boundaries by placing a .env file in the root directory:

• Broaden the Sandbox Root (e.g., allow the agent access to your entire home directory):

  LLAMASTUDIO_WORKSPACE_ROOT=/home/username
  

• Completely Disable Sandboxing (gives the agent full filesystem control based on the running process's permissions):

  LLAMASTUDIO_DISABLE_SANDBOX=true
  

Restart or reload the backend (lls reload) after modifying the .env file to apply changes.

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🧪 Testing Suite

LlamaStudio features both standard unit tests and comprehensive GGUF integration tests.

1. Standard Unit Tests

Verify local installation and confirm backend routing, regex parsing, and sandboxing safety behaviors by running our mock-based test suite:

python -m unittest discover tests

2. GGUF Model Integration Tests

For local environments containing active GPUs and downloaded models, you can run the full multi-model GGUF tool-calling integration suite to verify real-time execution robustness across various chat templates:

# Run GGUF model integration tests locally
./tests/test_all.sh

(These tests are automatically skipped in standard CI/CD environments and default pytest runs using @pytest.mark.skipif to keep pipeline checks fast.)

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🏗️ Project Structure

LlamaStudio/
├── start.py               # Main entrypoint (starts FastAPI + opens browser)
├── pyproject.toml         # Package metadata, CLI entrypoint, and dependencies
├── llamastudio.desktop    # GNOME/Linux desktop launcher metadata
├── llamastudio.svg        # Custom application vector icon
├── app/
│   ├── config.py          # Settings & dynamic path configurations
│   ├── main.py            # FastAPI backend endpoints & routing
│   ├── chat.py            # Conversations registry, templates & chat streaming
│   ├── downloader.py      # Async background download manager (chunked writes)
│   ├── model_manager.py   # Scans local paths and Hugging Face Hub
│   ├── server_manager.py  # llama-server subprocess process lifecycle controller
│   ├── logger.py          # Centralized logger
│   ├── tools.py           # Sandboxed local workspace tools for LLM agent use
│   └── templates/
│       └── index.html     # Interactive HTMX frontend interface
├── tests/
│   └── test_downloader.py # Comprehensive mocked test cases (100% passing)
└── imgs/
    ├── chat_interface.png # Screenshot: Main Chat interface
    ├── model_settings.png # Screenshot: Model explorer & settings
    └── discover_models.png# Screenshot: HF Discover & Downloader panel

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📄 License

LLamaStudio is open-source software licensed under the MIT License.