AI Poland - Mannmohan Muthuraman

Talk

Gemma-3 PEFT- LoRA-Driven Personalization for Adaptive Email Synthesis on Edge Inference

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.

About Mannmohan Muthuraman

Mannmohan Muthuraman serves as Artificial Intelligence leader at Santander, With a background in engineering at Standard Bank of South Africa (focusing on major releases and anti-fraud tech) in Johannesburg, he specializes in machine learning, spectral architecture optimization, and dynamic backpropagation. Recognized as a LinkedIn Top Voice in AI, he has authored publications like “Dynamic Spectral Backpropagation” (arXiv) and “Spectral Architecture Optimization” (IEEE), presented at conferences including IBA Architect and Canadian EdTech Summit, and secured patents for cardless transactions (5F Auth). His awards include IIT accreditation for AI work and wins in innovation programs at Standard Bank and Big Four firms. He’s also an MIT and IIT alumnus and have worked in several continents.

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Mannmohan Muthuraman

Talk

Gemma-3 PEFT- LoRA-Driven Personalization for Adaptive Email Synthesis on Edge Inference

About Mannmohan Muthuraman

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Meet our first speakers for 2025! More to be announced soon. Interested in joining? Apply below!

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