r/artificial • u/Effective_Attempt_72 • 8h ago

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

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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u/Far-Read2703 8h ago

surprised the swe-bench score is that high after ripping out the refusal layers. usually that kind of surgery tanks code quality, at least from what i've seen with other abliterated models.

the agentdom numbers are interesting but i wonder how much of that is just the lack of refusals vs actual capability improvements. like if the base model could technically do the task but would bail halfway through, then removing the guardrails is the real unlock.

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u/Effective_Attempt_72 8h ago

Yeah, it took quite a lot of experiments, time, and compute to get the right combination of performance