alicankiraz1/ms-swift

SWIFT ( Scalable lightWeight Infrastructure for Fine-Tuning ) is a toolkit that helps you customize and train large language models (LLMs) and multimodal models (models that can handle text, images, video, and audio). Instead of building an AI model from scratch or settling for a generic, off-the-shelf model, SWIFT lets you take an existing one, like Qwen, DeepSeek, or Llama, and adapt it with your own data so it knows what your business or project needs. It covers the full workflow: training, evaluating, and deploying the model. At a high level, the tool works by letting you point it at a pre-trained model and a dataset, then running a single command to kick off the training. It supports a wide range of training approaches, from "lightweight" fine-tuning (which tweaks just a small part of the model to save computing power and memory) to full retraining of all parameters. It also supports more advanced techniques like reinforcement learning (teaching the model through rewards) and preference alignment (teaching it to respond the way humans prefer). It comes with 150+ built-in datasets, or you can use your own. The tool is built for teams who want to adapt AI models for specific use cases without becoming deep-learning experts. For example, a startup could use it to train a customer-support bot on their own help docs, or a product team could fine-tune a vision model to recognize their specific product images. It also includes a web-based interface, so less technical users can handle training and deployment through a UI rather than the command line. What's notable is how much the project packs in while trying to stay accessible. It supports hundreds of models, multiple hardware types (including consumer GPUs and specialized chips), and various memory-saving techniques that let you train a 7-billion-parameter model with as little as 9GB of VRAM. The tradeoff is that the sheer breadth of features and configuration options means there's a lot to navigate, though the web UI and quick-start commands aim to ease that burden.