RE: https://arxiv.org/pdf/2309.05463.pdf RE: https://arxiv.org/abs/2309.05463
Cascading Model Paradigm
At its core, the paradigm revolves around a two-step process:
Broad Foundation Training: Initiate with a vast and diverse dataset to pre-train a foundational model, ensuring it amasses a wide-ranging knowledge base. Targeted Specialization: Subsequently, zoom into niche domains, refining the foundational model. Post fine-tuning, identify and eliminate components that are superfluous for the specialized task at hand. The streamlined model is then set as a precursor, paving the way for a series of specialized successors, each optimized for its designated task. This process mirrors the digital copying revolution, wherein the ability to create infinite, flawless replicas offers opportunities for both reuse and modification. Analogously, the vast knowledge inculcated in LLMs can be channeled, moulded, and repurposed to spawn models tailored for niche applications.
Anticipated Advantages:
- Compact models are more energy-efficient and reduce compute needed.
- Bespoke generation models.
- Scaling with a framework that is inherently iterative, facilitating progressive specialization.
Use meta-learning for rapid adaptability, and phased phine-tuning approaches.
Self-Tuning and Plasticity: Imagine a model that exhibits "self-tuning" capabilities. Such a model, bounded by appropriate constraints, continually refines itself based on new interactions. Over successive generations, it undergoes a form of "reduction", optimizing its internal vectors. This generational reduction not only trims the model but also makes it more "plastic", allowing it to adapt and reshape more seamlessly. This plasticity is pivotal in enabling the model to evolve in sync with dynamic data environments.
Case in Point: Experiments such as Phi-1.5 lend credibility to this vision. This model, equipped with a modest 1.3 billion parameters, when fed with refined synthetic datasets, has displayed capabilities that rival, if not surpass, its colossal counterpart, GPT-3, which boasts 175 billion parameters.
The cascading model paradigm suggests a SOON where AI accessibility is not a privilege of the few but a widespread reality. By meticulously orchestrating knowledge transfers and embracing generational reductions, we can envision an era of AI models that are not just smaller but are also agile, adaptable, and tailor-made for specific tasks. Kinda that quadcomm edge computing paradigm, of small devices connected to bigger ones. But more gross, throbbing llms.