Alignment dataset tracked in the /datasets sub-catalog.
auditkit-testrun-redteam
Built using AuditKIT — evaluate any model on any dataset and any task.
Method
redteam
Model
<auditkit.model.EchoModel object at 0x781f13372e40>
Artifact
redteam
Published
2026-09-01 12:49 UTC
Usage
from datasets import load_dataset
ds = load_dataset("ram-lexsi/auditkit-testrun-redteam")
license: other
pretty_name: auditkit-testrun-redteam
task_categories:
|
Built using AuditKIT — evaluate any model on any dataset and any task.
| Method | redteam |
| Model | <auditkit.model.EchoModel object at 0x781f13372e40> |
| Artifact | redteam |
| Published | 2026-09-01 12:49 UTC |
from datasets import load_dataset
ds = load_dataset("ram-lexsi/auditkit-testrun-redteam")
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.
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("ram-lexsi/auditkit-testrun-redteam")from huggingface_hub import snapshot_download
snapshot_download(repo_id="ram-lexsi/auditkit-testrun-redteam", repo_type="dataset")git clone https://huggingface.co/datasets/ram-lexsi/auditkit-testrun-redteamEvery 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.