Ludwig

Ludwig

Declarative deep learning framework built for scale and efficiency.

https://ludwig.ai/latest/

 

What is Ludwig?¶

Ludwig is a low-code framework for building custom AI models like LLMs and other deep neural networks.

Key features:

• 🛠 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. Support for multi-task and multi-modality learning. Comprehensive config validation detects invalid parameter combinations and prevents runtime failures.
• ⚡ Optimized for scale and efficiency: automatic batch size selection, distributed training (DDP, DeepSpeed), parameter efficient fine-tuning (PEFT), 4-bit quantization (QLoRA), and larger-than-memory datasets.
• 📐 Expert level control: retain full control of your models down to the activation functions. Support for hyperparameter optimization, explainability, and rich metric visualizations.
• 🧱 Modular and extensible: experiment with different model architectures, tasks, features, and modalities with just a few parameter changes in the config. Think building blocks for deep learning.
• 🚢 Engineered for production: prebuilt Docker containers, native support for running with Ray on Kubernetes, export models to Torchscript and Triton, upload to HuggingFace with one command.

Ludwig is hosted by the Linux Foundation AI & Data.

 

LLM Fine-tuning

https://ludwig.ai/latest/getting_started/llm_finetuning/