Developer builds low-cost epistemic gate to counter LLM data poisoning in fine-tuning

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An independent developer has released an open-source tool called an epistemic gate, designed to prevent data poisoning attacks during the fine-tuning of large language models. The project, shared on DEV Community, was tested across five different model architectures and orchestrated entirely on a 2006 Toshiba laptop at no cost. The release includes fully reproducible notebooks on Kaggle, a technical whitepaper covering 16 experiments, and a corrective manual with formal mathematical loss specifications. The project is hosted on GitHub and is aimed at indie developers, researchers, and startups interested in safe, local fine-tuning of LLMs.

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