extract 0 0 0

NASK-PIB/harmful_prompts_sample

published by @NASK-PIB

Alignment dataset tracked in the /datasets sub-catalog.

Lifetime downloads
530
Last 30 days
27
Likes
1
Size
79 KB
Created on HF
2025-01-29
Age
1 years ago

Description

Harmful Prompts Sample Dataset

The dataset Harmful Prompts Sample is a subset of red-teaming queries generated using the Bielik-11B-v2 model. This subset was manually reviewed and annotated. The purpose of this dataset is to utilize the following columns—category, subcategory_activity, style, and style_detail—to query the LLM and generate harmful prompts following these criteria.

Columns

category – A category from Llama Guard 3B used to help generate adversarial… See the full description on the dataset page: https://huggingface.co/datasets/NASK-PIB/harmful_prompts_sample.

Tags

license:cc-by-sa-4.0size_categories:n<1Kformat:parquetmodality:tabularmodality:textlibrary:datasetslibrary:pandaslibrary:mlcroissantlibrary:polarsregion:us

README current version from Hugging Face


license: cc-by-sa-4.0

Harmful Prompts Sample Dataset

The dataset Harmful Prompts Sample is a subset of red-teaming queries generated using the Bielik-11B-v2 model. This subset was manually reviewed and annotated. The purpose of this dataset is to utilize the following columns—category, subcategory_activity, style, and style_detail—to query the LLM and generate harmful prompts following these criteria.

Columns

  • category – A category from Llama Guard 3B used to help generate adversarial prompts.
  • subcategory_activity – A more detailed activity category that improves the adversarial prompt generation process.
  • style – The style type in which the prompt was generated.
  • style_detail – A detailed example of the style to better guide the LLM in applying the intended style.
  • prompt – The generated adversarial prompt.
  • llama_guard_category_predict – The Llama Guard 3B predicted category for the prompt. - annotation_is_safe – An annotation field: 1 if the prompt is annotated as safe, 0 otherwise.
  • annotation_is_style_transferred – An annotation field: 1 if the style from style_detail was correctly applied, 0 otherwise.
  • annotation_category – An annotation field containing one of the 14 categories from Llama Guard 3B if annotation_is_safe was set as 0.

README history 5 revisions

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.

  1. 2025-02-05Update README.md7313ee91.6 KB
    Loading...
  2. 2025-02-03Update README.md8c9cdd01.5 KB
    Loading...
  3. 2025-01-30Upload dataset5f116df687 B
    Loading...
  4. 2025-01-29Upload dataset367a352688 B
    Loading...
  5. 2025-01-29initial commit13060180 B
    Loading...

Used by abliterated models none yet

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.

Related datasets same stage · extract

Read further

Metadata

License
cc-by-sa-4.0

Repository

Download the dataset

0

Copy any of these snippets into your notebook or terminal. All three fetch directly from Hugging Face using your own credentials.

Recommended: datasets library (Python)
from datasets import load_dataset
ds = load_dataset("NASK-PIB/harmful_prompts_sample")
Raw snapshot (Python)
from huggingface_hub import snapshot_download
snapshot_download(repo_id="NASK-PIB/harmful_prompts_sample", repo_type="dataset")
git clone (requires git-lfs)
git clone https://huggingface.co/datasets/NASK-PIB/harmful_prompts_sample
Catalog is the map. Apps are the tools.

Run models on your own machine, not in the cloud.

Every 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.

Open in Abliteration