Gemma-3 PEFT for Personalized Email Generation on Edge
This covers fine-tuning Google’s Gemma-3 using PEFT (LoRA/QLoRA) for custom email synthesis. Key topics: Motivation (structured responses, accuracy > RAG, domain expertise); Approaches (full vs. PEFT); Parameters (quantization, rank r=4-64); Memory Optimization (Gemma-3 12B: 48GB to 6GB via NF4); LoRA (97% param reduction via low-rank matrices); QLoRA (quantize + adapt for efficiency); Advanced Tips (double quant, paged optimizers). Includes step-by-step LoRA fine-tuning guide and demo on Ollama for edge inference. Focus: Efficient, personalized AI on resource-limited devices.
Vice President of the Board, SpeakLeash, Bielik.ai | Poland
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Strategy & Digital Transformation, MBA IT Programme Director, Kozminski University | Poland
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Trainer & Consultant, AngularArchitects.io | Austria
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Google Developer Expert, Author, Podcaster, NG/JS/AI POLAND | Poland
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