Parameter-Efficient Fine-Tuning (PEFT)
A family of techniques that adapt large pre-trained models to specific tasks by modifying only a small fraction of parameters, dramatically reducing compute and memory requirements.
PEFT methods include LoRA, adapters, prefix tuning, and prompt tuning. They typically update 0.01-1% of model parameters while achieving 90-99% of full fine-tuning performance. PEFT has democratized LLM customization — organizations can adapt 70-billion-parameter models on consumer hardware. The Hugging Face PEFT library has been downloaded millions of times. These techniques are essential for enterprise AI adoption, where customization costs must be manageable at scale.
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Related Terms
Artificial General Intelligence (AGI)
A hypothetical form of AI that can understand, learn, and apply knowledge across any intellectual task at or above human level, rather than being specialized for specific tasks.
AI Compute
The computational resources — primarily GPU and TPU processing power — required to train and run AI models, typically measured in FLOP (floating-point operations) or GPU-hours.
Capex (Capital Expenditure)
Long-term investment spending by companies on physical assets like data centers, GPU clusters, and networking infrastructure — the backbone of AI deployment at scale.
ChatGPT
OpenAI's conversational AI assistant, launched in November 2022, which catalyzed the current generative AI boom by demonstrating the capabilities of large language models to a mainstream audience.
Data Center
A facility housing computer systems and infrastructure used to process, store, and distribute data — increasingly built specifically for AI training and inference workloads.
Fine-Tuning
The process of further training a pre-trained AI model on a specific, smaller dataset to specialize it for a particular task or domain, requiring far less compute than training from scratch.
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