@h100envy: Google engineer explained how to fine-tune a tiny LLM from 46% to 90% accuracy on your phone in 21 minutes - better tha…
Summary
A Google engineer shares a method to fine-tune a Gemma 270M model from 46% to 90% accuracy in 21 minutes on a phone, using synthetic data, LoRA, int4 quantization, achieving 2000 tokens per second offline.
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Google engineer explained how to fine-tune a tiny LLM from 46% to 90% accuracy on your phone in 21 minutes - better than $1500 on-device AI bootcamps.
pick Gemma 270M -> generate synthetic task data -> fine-tune with LoRA -> quantize to int4 -> deploy to Pixel and hit 2000 tokens per second.
That loop is how a 270M model beats a 70B one on your task, running fully offline in your pocket.
Gemma 270M + synthetic data + LoRA + int4 quantization + on-device runtime - that’s the stack.
Watch and save it, then fine-tune your own tiny agent tonight.
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