Unsloth is a full-stack, local-first AI platform for fine-tuning and inference of large language models (LLMs), diffusion models, and audio models. It consists of two primary components: (1) the core `unsloth` Python library, which provides highly optimized LoRA/QLoRA/RL training with custom Triton kernels, and (2) the Unsloth Studio application, a Tauri desktop app with a React frontend (TanStack Router, Zustand) and a FastAPI backend (SQLite, structlog). The platform enables users to discover models from the Hugging Face Hub, download and manage them locally, run inference with chat, image/video generation, and RAG, fine-tune with a visual recipe editor, and export in multiple formats (GGUF, NVFP4). It also integrates external LLM providers (OpenAI, Anthropic, Ollama) and supports agentic tool loops via MCP and Codex transports. The codebase is extensively tested with both unit tests and source-level contract tests, and includes cross-platform desktop installers for Windows, macOS, and Linux.
llmfit is a comprehensive LLM model analysis and recommendation system that matches models to local hardware specifications. It detects system capabilities (CPU, RAM, GPU/VRAM), analyzes model requirements across multiple dimensions (memory fit, speed, quality, context), and provides recommendations for optimal model selection. The system features multiple interfaces including a terminal user interface (TUI) as the default mode, command-line interface, embedded web dashboard, and REST API. It supports various inference backends (Ollama, llama.cpp, MLX, Docker Model Runner, etc.), includes benchmarking capabilities to measure real-world performance, and enables users to share benchmark results with the community. The architecture follows a modular Rust workspace design with llmfit-core as the shared library containing hardware detection, model analysis, and provider abstractions, while interface-specific concerns are separated into llmfit-tui, llmfit-desktop, llmfit-web, and llmfit-python packages.