Framework-free prototype learner lets local LLMs learn corrections instantly, no fine-tuning
thisdudelikesAI · x · 2026-09-26
To fix the familiar problem of local LLMs reverting to wrong answers after a correction, a developer built Jayce, a framework-free prototype learner: it takes context vectors from the local LLM and keeps them in a fixed pool of 4,096 prototypes. A correction shifts the closest prototype toward the new example, and re-asking immediately reflects the fix.
Model weights never change and there's no fine-tuning job. It's written in NumPy and native Java — no PyTorch or TensorFlow — runs offline on consumer hardware, and the repo includes a chat demo using a local Qwen3-4B model.
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