A config-driven fine-tuning framework where you describe a training run in a YAML file. It supports the full range of techniques, from LoRA and QLoRA to full fine-tunes and preference optimisation, and scales to multi-GPU setups.
A fine-tuning toolkit known for broad model support and an approachable web UI, which makes it one of the friendliest ways to start fine-tuning. It covers LoRA, QLoRA, full fine-tunes, and preference optimisation across many model families.
A Python package for running and fine-tuning LLMs on Apple Silicon via the MLX framework, the native path for training on a Mac.
A fine-tuning library focused on speed and low memory use, with optimised kernels that make LoRA and QLoRA training much faster and lighter. It is the tool to reach for when you want to fine-tune on a single consumer GPU.