Creating a LLaMa 2 Agent Empowered with Wikipedia Knowledge
Large Language Models (LLMs) are one of the hottest trends in AI. They have demonstrated impressive text-generation capabilities, ranging from the ability to carry on conversations with human users to writing code. The rise of open-source LLMs such as LLama, Falcon, Stable Beluga, etc., has made their potential available to the wide AI community, thanks also to the focus on developing smaller and more efficient models that can be run on consumer-grade hardware.
One of the key ingredients contributing to the success of LLMs is the famous transformer architecture introduced in the revolutionary paper Attention Is All You Need. The impressive performance of state-of-the-art LLMs is achieved by scaling this architecture to billions of parameters and training on datasets comprising trillions of tokens. This pre-training yields powerful foundation models cable to understand human language that can be further fine-tuned to specific use cases.
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