Adapt pretrained language models with compact trainable updates and assess quality, compute, and reproducibility trade-offs.
Examine parameter-efficient fine-tuning from its motivation and low-rank adaptation through PEFT workflows and quantized LoRA. Compare frozen base weights with trainable adapters, make target modules and data choices explicit, and design evaluations separating task gains from memorization, quantization effects, and deployment costs. Original analysis accompanies English-audio uploads; it is not transcript text.
Explain how parameter-efficient methods reduce trainable parameters while adapting a pretrained model. Compare LoRA and quantized LoRA in memory, optimization, quality, and inference requirements. Design a leakage-aware evaluation with suitable fine-tuning baselines and deployment measurements.
Experience with neural networks, transformer language models, and supervised fine-tuning is recommended. Gradient optimization, tokenization, and GPU memory familiarity helps; no particular library is required.
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Lesson 1 is free
4 lessons · Advanced · Full course.
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