Ludwig
What it is
Ludwig is a declarative deep learning framework that allows users to build, fine-tune, and deploy AI models (including LLMs and multimodal models) using YAML configuration files. It abstracts away complex boilerplate code by providing a high-level interface for tasks like instruction tuning, time-series forecasting, and image segmentation.
The problem it solves
Reduces the complexity and amount of code required to build, train, and deploy machine learning models, especially for complex tasks like LLM fine-tuning.
What you could use it for
- Fine-tuning Large Language Models (LLMs) using LoRA or QLoRA
- Time-series forecasting with state-of-the-art encoders
- Multimodal model fine-tuning (e.g., LLaVA, Qwen2-VL)
- Image segmentation and tabular data classification
- Distributed model deployment using Ray Serve or KServe
Read the source ↗Back to the library →
Summary written by an AI model from the source; check the source before you rely on it.