Parameter-Efficient Fine-Tuning
Parameter-Efficient Fine-Tuning (PEFT) refers to techniques that adapt large models by updating only a small subset of parameters. Methods like LoRA, adapters, and prefix tuning enable fine-tuning with minimal compute.
Understanding Parameter-Efficient Fine-Tuning
Parameter-Efficient Fine-Tuning (PEFT) encompasses techniques that adapt large pre-trained models to specific tasks by updating only a small fraction of the total parameters, dramatically reducing computational cost and memory requirements. Methods like LoRA (Low-Rank Adaptation) insert small trainable matrices into frozen transformer layers, while adapters add lightweight modules between existing layers, and prefix tuning prepends learnable tokens to the input. These approaches achieve performance comparable to full fine-tuning while training less than 1% of the original parameters, making it feasible to customize massive language models on consumer hardware. PEFT is particularly valuable when deploying multiple task-specific variants of a single base model, as each adaptation requires storing only a small set of additional weights. The technique has become essential in the era of billion-parameter models, enabling broader access to fine-tuned AI capabilities without requiring extensive computational infrastructure.
Category
Generative AI
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Chain of thought is a prompting technique that encourages large language models to break down complex reasoning into intermediate steps. This approach significantly improves performance on math, logic, and multi-step reasoning tasks.
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ChatGPT is an AI chatbot developed by OpenAI that uses large language models to generate human-like conversational responses. It became one of the fastest-growing consumer applications in history after its launch in November 2022.
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A diffusion model is a generative AI model that creates data by learning to reverse a gradual noise-adding process. Diffusion models power state-of-the-art image generation systems like Stable Diffusion and DALL-E.
Discriminator
A discriminator is the component of a GAN that learns to distinguish between real and generated data. It provides feedback to the generator, creating an adversarial training dynamic that improves output quality.
Few-Shot Prompting
Few-shot prompting provides a language model with a small number of input-output examples in the prompt to demonstrate the desired task format. This technique helps models understand task requirements without any fine-tuning.
Foundation Model
A foundation model is a large AI model trained on broad data that can be adapted to a wide range of downstream tasks. GPT-4, Claude, Gemini, and DALL-E are examples of foundation models that serve as bases for specialized applications.
GAN
A GAN (Generative Adversarial Network) is a generative model consisting of two competing neural networks — a generator and a discriminator. GANs produce realistic synthetic data by training these networks in an adversarial game.