Experiment with Local LLMs Improves Household Question Categorization Accuracy

Jun 21, 2026

AI Summary

A project aimed at enhancing a chatbot's ability to categorize household questions has shown significant improvements in accuracy through fine-tuning a small local LLM, Qwen 3:0.6B. Initial tests revealed a low accuracy of 10%, but after fine-tuning, accuracy increased to approximately 92%, demonstrating the potential of small models in specific applications.

A personal project involved developing a chatbot to answer household-related questions using a local LLM.
The chatbot utilizes a vector database and categorizes questions into metadata categories to improve search efficiency.
Two versions of the Qwen model were used: Qwen 3:4B for general answering and Qwen 3:0.6B for categorization.
The fine-tuning process employed the Unsloth framework, with an initial dataset of about 850 entries split into training, evaluation, and test sets.
The baseline model achieved only 10% accuracy in categorizing questions, indicating limitations in using the model without fine-tuning.
After fine-tuning, the model's accuracy improved to 79%, and further adjustments raised it to approximately 92%.
Changes included using fixed, non-overlapping category identifiers to enhance prediction accuracy.
Despite improvements, some categories still produced incorrect classifications, suggesting the need for further refinement of training data.
The project also prompted consideration of alternative classification methods, such as Logistic Regression, for future experiments.

fine-tuningllmcategorizationqwenlocal model

Experiment with Local LLMs Improves Household Question Categorization Accuracy

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