Training data for trust & safety, security, and defense teams.
Generate labeled datasets at scale: harmful-content classifier rows, adversarial prompts, preference pairs, eval cases, and the edge cases default LLMs refuse to write. Every row is schema-validated with policy labels, severity tiers, provenance, and export paths your training pipeline already uses.
Generate 10,000 realistic customer support conversations about refunds and billing edge cases.
billing-support-training.jsonl
10,000 rows · 4.2 MB · ready
Built for teams with legitimate hard-to-source labels.
Trust & safety operations
Balanced toxic and non-toxic examples, marketplace fraud scenarios, coded-harassment variants, and reviewer-ready reason codes for classifier training.
Security and AI red teams
Prompt-injection corpora, jailbreak regression sets, tool-misuse examples, and adversarial eval rows for authorized testing workflows.
Defense and regulated research
Governed dataset generation with policy labels, audit logs, scoped keys, and export controls for teams that need access without losing oversight.
The categories that block your training pipeline.
Coded harassment
Multi-lingual dog-whistles and ingroup-coded slurs for moderation classifier training.
Phishing emails
Synthetic phishing across urgency, authority, and pretexting tactics for detection eval.
Prompt injections
Direct, indirect, ASCII-smuggling, and tool-confusion vectors for AppSec red-team training.
Adversarial edge cases
Refusal-eval prompts, policy edge cases, and known-jailbreak corpora for safety regression sets.
Reasoning included.
Most frontier APIs hide chain-of-thought out of distillation paranoia. We don't. Toggle Thinking in the console and the model's reasoning lands on every row as a schema-aware sidecar, ready to feed your next reasoning model.
Grounded in the live web.
Generate against today's web, not the model's frozen pre-training cutoff. Toggle Web search in the console and the sources field on every row carries the URLs the model actually fetched, with titles and fetched-at timestamps. Citations stay with the row through export.
Preview before you commit.
Every job starts with a paid 3-row preview generated against your spec, schema, and toggles. We charge real preview credits, then estimate the full run as the highest-cost preview row × your row target plus a reserve buffer. Top up if short. Promote to full job if it looks right.
| # | sample prompt | tokens in/out | cost |
|---|---|---|---|
| 1 | Multi-lingual coded harassment, tier-2 severity. | 1,402 / 812 | $0.0052 |
| 2 | RAG-grounded answer about Article 53 timeline. | 1,538 / 921 | $0.0064max |
| 3 | Deepfake-script detector seed, voice-clone variant. | 1,318 / 784 | $0.0049 |
In the format your pipeline already speaks.
Push to where your training pipeline lives.
Stream a run straight to the bucket, dataset hub, or notebook you already use. No middleman, no manual upload step.
Hugging Face Hub
One-click push to a private or public dataset card.
Kaggle
Push to a private Kaggle dataset for competitions and notebooks.
Amazon S3
Stream directly to your bucket. SSE-KMS supported.
Google Cloud Storage
Stream to GCS with service-account auth and CMEK.
Azure Blob
Write straight to a container with managed identity.
Direct download
Grab a signed URL and pull the run from your training box.
Typed once. Validated every row.
Define the dataset shape once. Every generated row is validated before it leaves the workspace, with provenance, tier, and policy label attached. No bespoke clean-up step downstream.
{ "schema": "preference-pair-v3", "fields": [ { "name": "prompt", "type": "string" }, { "name": "chosen", "type": "string" }, { "name": "rejected", "type": "string" }, { "name": "policy", "type": "string" }, { "name": "tier", "type": "int" } ] }
{ "prompt": "Rewrite this support ticket in a civil tone.", "chosen": "I understand your frustration. Let's solve the refund...", "rejected": "Stop being so rude. Read the FAQ.", "policy": "civil-tone-v2", "tier": 1 }
One line in your training script.
Once a run is exported, load it the same way you load every other dataset: Hugging Face datasets, the OpenAI fine-tune API, or your own RLHF stack.
from datasets import load_dataset ds = load_dataset( "abliteration/runs/billing-support-training", split="train", ) trainer.train(dataset=ds)
Paid previews, then production-scale runs.
Generate up to 1M records per job. Tiered by records-per-month, with usage-based scaling for enterprise.
Frequently asked questions.
What datasets can I generate with Abliteration's training-data API?
Preference pairs (DPO/ORPO), eval sets, classifier examples, and adversarial corpora — plus any custom labelled dataset matching a schema you define. Up to 1 million records per job.
Are reasoning traces included on every row?
Yes. Toggle Thinking in the console and the model's chain-of-thought lands as a schema-aware sidecar on every generated row — the field most frontier APIs strip out. Useful for distillation, RLHF teacher signals, and process-supervision research.
Where can I export the generated data?
One-click push to Hugging Face datasets, Kaggle, Amazon S3, Google Cloud Storage, Azure Blob, or grab a signed URL for direct download. No middleman, no manual upload step.
How is cost estimated before a full run?
Every job starts with a paid 3-row preview against your spec, schema, and toggles. Abliteration charges real preview credits, then estimates the full run as the highest-cost preview row × your row target plus a reserve buffer. Promote to a full job only when the preview and estimate look right.
Why use Abliteration instead of another generation API?
Provider-default APIs refuse the rows your classifier actually needs — synthetic phishing for fraud detection, coded harassment for moderation, jailbreak attempts for safety eval. Abliteration generates the entire taxonomy schema-locked, with reasoning, citations, and provenance on every row.
Try the model that doesn’t say no.
Free tier. OpenAI-compatible. Policy Gateway when you scale.