JEV System-1 Union Training Set (PRIVATE staging)
Per-domain union of the Jev decision-distillation corpora, built 2026-10-10/11 for the
JEV-27B Uncensored System-1 retrain. PRIVATE until Will approves any public flip.
What it is
764,626 rows, 0 truncation at 1024 tokens. Each row: a decision prompt (kind =
noul/score/choice over ...[truncated]
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("SargeDev/jev-system1-union-train")from huggingface_hub import snapshot_download
snapshot_download(repo_id="SargeDev/jev-system1-union-train", repo_type="dataset")git clone https://huggingface.co/datasets/SargeDev/jev-system1-union-trainShowing the first 5,000 files. Full list on Hugging Face ↗
No file list for this dataset yet.
The file list of this gated dataset is private on Hugging Face.
Per-domain union of the Jev decision-distillation corpora, built 2026-10-10/11 for the
JEV-27B Uncensored System-1 retrain. PRIVATE until Will approves any public flip.
764,626 rows, 0 truncation at 1024 tokens. Each row: a decision prompt (kind =
noul/score/choice over ...[truncated]
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.
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.
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.