SWIFT (Scalable lightWeight Infrastructure for Fine-Tuning)
Introduction
SWIFT stands for "Scalable lightWeight Infrastructure for Fine-Tuning". It’s designed to simplify the process of working with a wide range of large language models (LLMs), over 350, and more than 100 multimodal large models (MLLMs). This infrastructure supports various stages in the model lifecycle including training, fine-tuning, leveraging reinforcement learning with human feedback (RLHF), inference, evaluation, and deployment. This offers researchers and developers a comprehensive framework enabling the seamless transition from model creation to application deployment.
The framework incorporates lightweight training solutions using PEFT and hosts a comprehensive Adapters library. This library aids in adopting the latest training techniques such as NEFTune, LoRA+, and LLaMA-PRO. These resources are easily integrated into custom workflows without necessitating the use of SWIFT’s training scripts, making them flexible for varied user applications.
To assist users unfamiliar with deep learning concepts, SWIFT provides an intuitive Gradio web UI. This UI facilitates the management of training and inference tasks. It also comes with deep learning courses and best practices tailored for beginners. Access the SWIFT web UI at Huggingface space or ModelScope studio for a practical hands-on experience.
SWIFT also features an extensive collection of documentation to guide users through its functionalities. The documentation is accessible in both English and Chinese to cater to a global audience.
Groups
SWIFT encourages community interaction and offers platforms for engagement. Users can join the SWIFT community via Discord to connect with developers and share insights. For users who prefer platforms native to China, a WeChat group is also available.
News
The SWIFT project is actively developed and frequently updated with new capabilities:
- As of late 2024, SWIFT added support for various new model types such as aya-expanse, emu3-chat, and llama3.2 series.
- Innovative features include reward modeling and PPO training for both LLM and MLLM models.
- Significant memory optimization is provided by Liger functionality, allowing reduction of memory usage by up to 60%.
- Enhanced performance through the inclusion of acceleration methods like vLLM for rapid inference.
- Rich training algorithms such as DPO, ORPO, SimPO, and CPO are available for vision MLLMs.
- Recent adoption of back-end infrastructures like DeepSpeed-Zero3 further optimizes handling of multi-modal large models.
- Continual support is emphasized on new datasets and alignment algorithms to improve training efficacy and model accuracy.
These updates highlight SWIFT’s dedication to providing state-of-the-art tools for machine learning practitioners, researchers, and industry professionals.
Installation and Getting Started
The installation of SWIFT is straightforward, compatible with systems running Python 3.8 or higher and PyTorch 1.12 onward. Detailed installation instructions and a comprehensive getting started guide are provided in the documentation.
Classroom
For those interested in expanding their knowledge, SWIFT offers educational resources, including practical lessons and best practices to guide new users from foundational concepts to advanced implementation techniques.
License
SWIFT is available under a permissive license ensuring that users can freely utilize, modify, and share the project within the constraints of the license.
Citation
For academic or research utilization, proper citation of the SWIFT project is encouraged to acknowledge the contributions of its developers and community in supporting open-source machine learning advancements.
These facets make SWIFT an integral tool for enabling efficient model fine-tuning infrastructure geared towards a diverse range of use cases in the AI field.