Alignment dataset tracked in the /datasets sub-catalog.
hh-rlhf中文翻译版本
基于Anthropic论文Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback 开源的helpful 和harmless数据，使用翻译工具进行了翻译。hh_rlhf_train.jsonl 合并中英文训练集数据 清洗过后17万条hh_rlhf_test.jsonl 合并中英文测试集数据 清洗过后9千条harmless_base_cn_train.jsonl 42394条harmless_base_cn_test.jsonl 2304条helpful_base_cn_train.jsonl 43722条helpful_base_cn_test.jsonl 2346条
实验报告
相关rlhf实验报告:https://zhuanlan.zhihu.com/p/652044120
基于Anthropic论文Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback 开源的helpful 和harmless数据，使用翻译工具进行了翻译。
hh_rlhf_train.jsonl 合并中英文训练集数据 清洗过后17万条
hh_rlhf_test.jsonl 合并中英文测试集数据 清洗过后9千条
harmless_base_cn_train.jsonl 42394条
harmless_base_cn_test.jsonl 2304条
helpful_base_cn_train.jsonl 43722条
helpful_base_cn_test.jsonl 2346条
相关rlhf实验报告:https://zhuanlan.zhihu.com/p/652044120
Every night we snapshot the README of every dataset in the catalog. When the SHA changes we archive the new version and diff it against the last. This is the evolving thought record of the alignment-data field: what the author decided to say about the corpus, and how that framing shifted over time.
No abliterated models in our catalog have declared this dataset in their YAML metadata yet. This can mean the dataset is used but not declared, is used outside abliteration workflows, or is new. The link will populate automatically as models are indexed.
Threads posted by the community on Hugging Face: bug reports, questions about usage, pull requests on the README, license clarifications. Click any thread to expand and read the full conversation inline. Newest first.
Copy any of these snippets into your notebook or terminal. All three fetch directly from Hugging Face using your own credentials.
from datasets import load_dataset
ds = load_dataset("dikw/hh_rlhf_cn")from huggingface_hub import snapshot_download
snapshot_download(repo_id="dikw/hh_rlhf_cn", repo_type="dataset")git clone https://huggingface.co/datasets/dikw/hh_rlhf_cnEvery model page has an "Open in Abliteration" button that hands the model directly to the first-party desktop client, at the quantization your rig can actually run. No API keys, no subscription, no prompt leakage.