We released an abliterated + fine-tuned GLM-5.2. High scores on adversarial benchmarks while keeping coding performance.

Reddit r/artificial Models

Summary

Released an abliterated and fine-tuned version of GLM-5.2 (abliterated-model-large) that achieves high scores on adversarial and agent benchmarks while maintaining coding performance. The model is available via API with zero data retention and no built-in policy.

We just shipped abliterated-model-large. It is GLM-5.2 with the refusal directions removed, then fine-tuned specifically for long adversarial and agent-style tasks. The goal was a model that does not bail out when the work gets technical or offensive in nature. Numbers from our evals: CyberGym: 84.2% AgentHarm compliance: 86.2% (zero refusals in the published set) AgentDojo utility: 97.5% SWE-bench Verified: 81.2% Terminal-Bench 2.1: 80.1% It is available as an API (OpenAI and Anthropic compatible). Zero data retention is the default. The model itself has no built-in policy. You set the rules. Full write-up with more detail is here: https://abliteration.ai/blog/introducing-abliterated-model-large Curious what people think of the AgentHarm and CyberGym numbers relative to other models that still refuse a lot of these tasks.
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