Alignment dataset tracked in the /datasets sub-catalog.
数据集 unalignment/toxic-dpo-v0.2 的中文版本,并基于self-Instruct和evol-Instruct继续扩展。
这是一个高度有害的数据集，旨在通过一些示例来说明如何使用 SFT 轻松地对模型进行去审查/取消对齐。
数据生成代码：https://github.com/Minami-su/character_AI_open/tree/main/toxic-Instruction
使用限制请参照原数据集的 Usage restriction。
license: cc-by-4.0
tags:
数据集 unalignment/toxic-dpo-v0.2 的中文版本,并基于self-Instruct和evol-Instruct继续扩展。
这是一个高度有害的数据集，旨在通过一些示例来说明如何使用 SFT 轻松地对模型进行去审查/取消对齐。
数据生成代码：https://github.com/Minami-su/character_AI_open/tree/main/toxic-Instruction
使用限制请参照原数据集的 Usage restriction。
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.
Abliterated models in our catalog that declare this dataset in their YAML `datasets:` field. Sorted by downloads. This back-reference is unique to abliteration.org: Hugging Face does not surface it.
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("Minami-su/toxic-sft-zh")from huggingface_hub import snapshot_download
snapshot_download(repo_id="Minami-su/toxic-sft-zh", repo_type="dataset")git clone https://huggingface.co/datasets/Minami-su/toxic-sft-zhEvery 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.