Uncensured AI abliterated models: what to check before deployment
A practical guide to Uncensured AI's abliterated model catalog, reported 250–300 tokens/s and 0% refusal rate, with measurement limits explained.
Read the article ↗ A field guide to abliterated AI
Less refusal.
Better evidence.
Understand what abliteration changes, what it can cost, and how to evaluate the models you actually use.
Explore the journalFig. 01 / Directional ablationConceptual
Remove a component.Then measure what changed.
12 articlesMechanisms, methods & models
40 fixed promptsA downloadable evaluation kit
Sources includedClaims with their context
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EDITOR'S SELECTION / 01—02How to measure abliteration damage with 40 fixed prompts
A reproducible 40-prompt protocol for comparing base and abliterated models, with scoring rules and a downloadable results template.
Read the article ↗The journal
12 ARTICLESHow to measure abliteration damage with 40 fixed prompts
A reproducible 40-prompt protocol for comparing base and abliterated models, with scoring rules and a downloadable results template.
What can change after abliteration? Three capabilities to test
Test long reasoning, nested instructions and strict output formats separately when evaluating an abliterated language model.
Uncensured AI abliterated models: what to check before deployment
A practical guide to Uncensured AI's abliterated model catalog, reported 250–300 tokens/s and 0% refusal rate, with measurement limits explained.
Base vs abliterated: the comparison mistakes that change the result
Control chat templates, generation budgets, seeds and quantization when comparing original and abliterated language-model checkpoints.
Refusal vector, refusal rate and KL divergence: a practical glossary
Clear definitions of refusal direction, refusal rate, KL divergence, over-refusal and generation throughput for model evaluation.
How to read an abliterated model card
The provenance, evaluation and serving details to check before trusting a model card that claims abliteration or zero refusals.
Abliteration and the refusal direction: the mechanism explained
Understand the geometric idea behind abliteration, what removing a direction means and why the result still needs evaluation.
Residual refusals: how to identify what a model still declines
A practical taxonomy for refusal, partial answers, over-refusal and service failures after abliteration, with benign examples.
Abliteration in 2026: what the research does and does not establish
A source-based overview of directional refusal interventions and 2026 research, with practical limits for model evaluation.
Abliterated vs quantized models: two different changes to evaluate
Abliteration changes refusal-related behavior; quantization changes numerical representation. Learn how to separate their effects in a comparison.
Are small abliterated models worth using? Test the workload first
How to evaluate smaller abliterated models without assuming a universal 30B-parameter threshold for quality or capability loss.
Uncensured AI's 0% refusal rate: a reproducible evaluation plan
Interpret Uncensured AI's operator-reported 0% refusal rate and use a transparent 120-run plan to evaluate three model configurations.
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