Ludwig is a low-code framework for building custom AI models like LLMs and other deep neural networks. The Ludwig allows you to build custom models with ease. A declarative YAML configuration file is all you need to train a state-of-the-art LLM on your data and its support for multi-task and multi-modality learning. You can also optimize for scale and efficiency, since it also provides automatic batch size selection, distributed training (DDP, DeepSpeed), parameter efficient fine-tuning (PEFT), 4-bit quantization (QLoRA), and larger-than-memory datasets. By supporting hyperparameter optimization, explainability, and rich metric visualizations, you retain full control of your models down to the activation functions. It is modular and extensible and is engineered for production (Docker, HuggingFace).
Link: https://ludwig.ai/latest/
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