d3-edge (0.6B) fine-tuned outperformed d3 (27B) decision model

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fine-tuning small-models lora sft model-comparison vllm decision-models

Summary

A 0.6B parameter d3-edge model, fine-tuned with SFT + LoRA on a few thousand examples, outperformed the 27B d3 decision model on typed decisions and nearly matched it on Banking77, highlighting the value of fine-tuning small specialized models over large general-purpose ones.

Took vLLM-SR d3-edge (0.6B), fine-tuned it with simple SFT + LoRA, and compared it against the much larger d3 (27B). With just a few thousand training examples, the 0.6B model actually outperformed the 27B model on typed decisions and nearly matched it on Banking77. I strongly believe we should be fine-tuning small, specialized decision models rather than using large general-purpose models for every task. Colab - https://colab.research.google.com/drive/1wze4PA3GBrxRLYhzzFK7czlmvAgRdA9P?usp=sharing
Original Article

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