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Show HN: Abliteration – made-to-order training data for classifiers and evals

Abliteration provides a platform for generating synthetic training and evaluation data via an OpenAI-compatible API, overcoming the limitations of refusal-tuned models. Its Policy Gateway offers custom rules, structured JSONL output, per-project quotas, and decision logs for use cases like safety classifier testing, fine-tuning pair creation, and adversarial training.

SourceHacker News AIAuthor: thomadev0
Show HN: Abliteration – made-to-order training data for classifiers and evals
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Key points

  • Refusal-tuned models can't generate adversarial data at scale
  • Abliteration allows custom policies to generate unrestricted training data
  • Structured JSONL output directly integrates with ML pipelines
  • Compliance features include decision metadata, reproducible runs, and zero data retention

Highlights and analysis are generated automatically and may contain errors. Check the original source.

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