LoRA (Low-Rank Adaptation)
LoRA is a parameter-efficient fine-tuning technique that trains only a small set of additional weights on top of a pre-trained model, making custom AI model training practical on consumer hardware.
How it works & Why it matters
Training a full LLM from scratch costs millions. LoRA avoids this by freezing the original model weights and injecting small trainable matrices (adapters) into each layer. The result: you can fine-tune a 7B parameter model on a single consumer GPU in a few hours, using as little as a few hundred examples of your domain-specific data. QLoRA goes further by quantizing the base model to 4-bit precision, cutting memory requirements in half. For businesses, this means a custom AI that speaks your industry's language, uses your templates, and follows your compliance rules, all trained on hardware that costs less than a month of cloud AI fees.
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