SIMURG Tool Claims to Reduce LLM Hallucinations by Intercepting Bad Outputs

A developer has shared early results using a tool called SIMURG to reduce hallucinations in locally run large language models. SIMURG sits in front of an OpenAI-compatible endpoint, monitors text generation in real time, and automatically aborts and retries requests when it detects the model going off track. The developer tested it with a heavily compressed 2-bit quantized version of Qwen3.8 27B running on a consumer-grade RTX 3060 GPU, a setup prone to generating hallucinations and garbled outputs. According to the developer, responses became noticeably more consistent after deploying SIMURG, with end users never seeing the problematic outputs. The tool is described as a 15-parameter machine learning model and is available via pip install, with early testing still ongoing across varied prompts.