openeurollm
propella-annotations
This dataset contains document annotations produced with propella-1-4b, a small multilingual LLM that annotates text documents across six categories: core content, classification, quality & value, audience & purpose, safety & compliance, and geographic relevance. The annotations can be used to filter, select, and curate LLM training data at scale.
Properties
Each document is annotated across 18 properties organized into six categories:
Category
Property
Description… See the full description on the dataset page: https://huggingface.co/datasets/openeurollm/propella-annotations.Dolci-Instruct-SFT-translatedsmoltalk2-decontaminated
Decontamination
This dataset is a decontaminated version of HuggingFaceTB/smoltalk2.
Benchmarks used
MATH500: HuggingFaceH4/MATH-500 (subset=default, split=test)
AIME24: HuggingFaceH4/aime_2024 (subset=default, split=train)
AIME25: math-ai/aime25 (subset=default, split=test)
AMC23: math-ai/amc23 (subset=default, split=test)
JEEBench: daman1209arora/jeebench (subset=default, split=test)
GPQADiamond: Idavidrein/gpqa (subset=gpqa_diamond, split=train)
LiveCodeBench:… See the full description on the dataset page: https://huggingface.co/datasets/openeurollm/smoltalk2-decontaminated.Nemotron-Post-Training-Dataset-v2-decontaminated
Decontamination
This dataset is a decontaminated version of nvidia/Nemotron-Post-Training-Dataset-v2.
Benchmarks used
MATH500: HuggingFaceH4/MATH-500 (subset=default, split=test)
AIME24: HuggingFaceH4/aime_2024 (subset=default, split=train)
AIME25: math-ai/aime25 (subset=default, split=test)
AMC23: math-ai/amc23 (subset=default, split=test)
JEEBench: daman1209arora/jeebench (subset=default, split=test)
GPQADiamond: Idavidrein/gpqa (subset=gpqa_diamond, split=train)… See the full description on the dataset page: https://huggingface.co/datasets/openeurollm/Nemotron-Post-Training-Dataset-v2-decontaminated.Dolci-Think-SFT-translated
Dolci-Think-SFT-translated
Machine translations of the Dolci-Think-SFT-32B dataset, produced with gemma-4-31B-it. The samples selected for translation are those where content_quality == "excellent" according to the propella annotations.
Columns
Each row is a translated conversation plus the result of a post-translation quality filter:
id — source record id.
messages — the translated conversation (list of {content, role}).
filter_pass — true if the row passed… See the full description on the dataset page: https://huggingface.co/datasets/openeurollm/Dolci-Think-SFT-translated.Dolci-Instruct-DPO-translated