⚗️ distilabel is a framework for synthetic data and AI feedback for AI engineers that require high-quality outputs, full data ownership, and overall efficiency.
-
Updated
Aug 9, 2024 - Python
⚗️ distilabel is a framework for synthetic data and AI feedback for AI engineers that require high-quality outputs, full data ownership, and overall efficiency.
ZYN: Zero-Shot Reward Models with Yes-No Questions
code for paper Query-Dependent Prompt Evaluation and Optimization with Offline Inverse Reinforcement Learning
distilled Self-Critique refines the outputs of a LLM with only synthetic data
Timo: Towards Better Temporal Reasoning for Language Models (COLM 2024)
Add a description, image, and links to the rlaif topic page so that developers can more easily learn about it.
To associate your repository with the rlaif topic, visit your repo's landing page and select "manage topics."