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.
Use Case · Synthetic Data
Generate training and eval data without refusals.
Fine-tuning pairs, eval sets, adversarial corpora — through the same OpenAI-compatible API your pipelines already use.
Generate the data your ML pipeline actually needs. We host the unrestricted model; your policy decides what each project can produce. Structured outputs and decision logs come standard.
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The problem
Why teams in synthetic data hit a wall.
Refusal-tuned models can't generate adversarial data
Training a safety classifier? You need examples of unsafe prompts. General-purpose APIs refuse to write them — leaving you with hand-curated datasets that don't scale.
Generation quality drops at scale
Off-the-shelf APIs apply unpredictable refusal rates that vary by topic and even by phrasing. Reproducible large-batch jobs become impractical.
No governance audit on what you generated
When a fine-tuning dataset ships into production, you need provenance: which policy, which prompts, which model. Most generation APIs offer no decision metadata.
How Policy Gateway helps
Built for synthetic data workloads.
Less-restricted inference, your rules
Generate the prompts and completions you actually need for training. Your policy decides what's in scope — not the provider's defaults.
Structured JSONL output
Generate fine-tuning pairs, eval entries, or labeled corpora directly in the format your training pipeline expects. No post-processing required.
Per-project quotas and key scoping
Issue a scoped key per dataset job. Track generation volume, cost, and decision history per project — and prove dataset provenance to your reviewers.
Examples
Scenarios from the field.
Eval set generation
Generate 10k labeled prompts for testing a safety classifier. Track every example with policy ID and reason code so QA can replay decisions.
Fine-tuning pair creation
Produce instruction/response pairs for vertical model fine-tuning. Same governed API; no refusal noise polluting your dataset distribution.
Adversarial training data
Generate jailbreak attempts and edge cases for safety training. Controlled, audited, reproducible — and isolated to the project key that paid for it.
Compliance & alignment
Designed for the frameworks your auditors care about.
Built so your dataset shipping reviews don't stall on questions about provenance.
Decision metadata per record
Policy ID, reason code, and key scope on every generated example.
Reproducible runs
Same prompt, same policy version → comparable output across batches.
Per-project quotas
Hard caps so a dataset job can't blow the budget.
JSONL-ready outputs
Structured straight into your training pipeline.
Zero data retention
Generated content not used for training or shared.
SOC 2 (in progress)
Enterprise audits underway.
See the Trust Center
Ready to bring governance to your synthetic data stack?
Talk to us about your deployment, or grab an API key and start building today.
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