argilla-io/distilabel
Distilabel is a framework for synthetic data and AI feedback for engineers who need fast, reliable and scalable pipelines based on verified research papers.
Label, clean and enrich text datasets with LLMs.
Appears on
Quick read
Latest capture 2026-09-08 10:55
0 paths
Agent instructions and tool configuration found in this repository.
No config files detected.
1 observed capture since 2026-09-08. Observed captures are shown by default.
Stars from first capture 0
Observed captures only
All tracked data
Observed snapshots
Observed snapshots
Nearest indexed repositories by embedding similarity.
Distilabel is a framework for synthetic data and AI feedback for engineers who need fast, reliable and scalable pipelines based on verified research papers.
Cleanlab's open-source library is the standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
LLM Finetuning with peft
Label Studio is a multi-type data labeling and annotation tool with standardized output format
Low-code framework for building custom LLMs, neural networks, and other AI models
A library of data loaders for LLMs made by the community -- to be used with LlamaIndex and/or LangChain