A user has developed a method for fine-tuning a small, 2-billion parameter language model on personal chat data, specifically a WhatsApp group conversation. While the resulting model is described as fun and capable of mimicking the group's slang and pacing, it lacks deep understanding and consistent coherence. The project, documented on GitHub as a cookbook, includes a reproducible pipeline, chat UI, and evaluation methods, with the best version achieving an 80% human win rate in human-vs-model tests. AI
IMPACT Demonstrates a method for personalizing LLMs with user-specific data, potentially improving chatbot engagement.
RANK_REASON User-developed tool/method for fine-tuning a small LLM on personal data.
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