FrameTrain

Local AI model training & fine-tuning — a cross-platform desktop app.

FrameTrain is a desktop application for training and fine-tuning AI models locally on your own hardware — no cloud required. It brings LoRA/QLoRA fine-tuning of Hugging Face models, PyTorch-based training with GPU acceleration (NVIDIA CUDA and Apple Silicon / Metal MPS), a visual neural-network builder, dataset management, live training monitoring and automatic model versioning together in one GUI.

Because training runs entirely on your machine, your datasets and models never leave your device — GDPR-compliant by design.

This is the official source repository for the FrameTrain desktop app. Ready-to-install builds are on the website and under Releases.

Features

Fine-tuning & training

• LoRA and QLoRA fine-tuning of large language models
• PyTorch-based training pipeline
• Import Hugging Face models or load local ones
• Export trained models, including GGUF for local inference
• Ollama integration for running results locally

Synapse Builder — visual neural networks

• Build custom architectures on a drag-and-drop canvas (powered by React Flow)
• Transformer, CNN and LSTM building blocks
• Train directly from the canvas

Data & workflow

• Dataset import, Parquet support, train/validation/test splitting
• Live training monitoring with loss curves
• AI training analysis & coach for diagnosing runs
• Automatic model versioning and comparison of training runs

Runs everywhere, stays private

• Windows, macOS and Linux
• NVIDIA CUDA and Apple Silicon (M1/M2/M3/M4 via Metal MPS) — no CUDA required on Mac
• Fully local and offline

See the app in action on the screenshots tour.

Download

Get the latest build for your platform:

• frame-train.com/download — recommended
• GitHub Releases — all versions

Requirements: a 64-bit OS; for LLM fine-tuning an NVIDIA GPU or an Apple Silicon Mac is recommended (8 GB unified memory minimum, 16 GB+ for 7B models). See the Apple Silicon guide for what your Mac can handle.

Build from source

FrameTrain is a Tauri 2 app with a React + TypeScript frontend.

Prerequisites

• Node.js 18+ and npm
• Rust (stable) — required by Tauri
• Platform tooling for Tauri (see Tauri prerequisites)

Setup

# install frontend dependencies
npm install

# run the desktop app in development (hot reload)
npm run tauri:dev

# build a production desktop binary for your platform
npm run tauri:build

Frontend-only commands (without the desktop shell) are also available:

npm run dev       # Vite dev server
npm run build     # type-check + build the web frontend
npm run preview   # preview the built frontend

Tech stack

• Shell: Tauri 2 (Rust) — small, secure, native binaries
• Frontend: React 18, TypeScript, Vite, Tailwind CSS
• Visual builder: React Flow (@xyflow/react)
• Training core: PyTorch, Hugging Face, PEFT/LoRA, QLoRA, BitsAndBytes
• Testing: Vitest, Testing Library

Project structure

src/          React + TypeScript frontend (UI, AI logic, datasets, Synapse Builder)
src-tauri/    Rust backend (native shell, AI proxy, file system, updater)
scripts/      Build and maintenance scripts

Learn more

• Documentation · Guides
• LoRA fine-tuning guide
• FrameTrain vs. MLX LoRA Studio vs. Unsloth
• FAQ · Changelog

License

FrameTrain is proprietary software. See LICENSE for the terms that apply to this repository.

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Made by FrameSphere.