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

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.

submitted by /u/Effective_Attempt_72
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