datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Magpie-Qwen2-Pro-200K-Chinese
Project Web: https://magpie-align.github.io/
Arxiv Technical Report: https://arxiv.org/abs/2406.08464
Codes: https://github.com/magpie-align/magpie
Abstract
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High-quality instruction data is critical for aligning large language models (LLMs). Although some models, such as Llama-3-Instruct, have open weights, their alignment data remain private, which hinders the democratization of AI. High human labor costs and a limited, predefined scope for prompting prevent… See the full description on the dataset page: https://huggingface.co/datasets/Magpie-Align/Magpie-Qwen2-Pro-200K-Chinese.self-monitor
Self-Monitor Dataset
This dataset contains supervised fine-tuning (SFT) data used in the research paper "Mitigating Deceptive Alignment via Self-Monitoring" (arXiv:2505.18807).
Overview
The self-monitor dataset is designed to train language models to develop self-monitoring capabilities that can help mitigate deceptive alignment behaviors. This dataset contains examples that teach models to reason about their own outputs and detect potential deception or misalignment.… See the full description on the dataset page: https://huggingface.co/datasets/PKU-Alignment/self-monitor.PKU-Alignment-Graphasynchow-code-aligned-minutes
AsynChow Code-Aligned Minutes
This dataset is a unit-normalized variant of the AsynChow data released with
fangru-lin/procedure_generalization_llm,
pinned to source commit d9bf3485cd41c1050d33471d922c826f474efec1.
It contains three aligned representations of each weighted DAG scheduling
problem:
natural: natural-language steps and precedence constraints;
graph: adjacency-list and duration-dictionary representation;
python: executable-style Python representation from the… See the full description on the dataset page: https://huggingface.co/datasets/PTTREP/asynchow-code-aligned-minutes.