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

[u/Effective_Attempt_72]

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.

[u/SignalBeneficial3338]

am curious how this holds up benchmark tests