From the course: Prompt Engineering with LangChain
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The LLM landscape
From the course: Prompt Engineering with LangChain
The LLM landscape
- [Instructor] Since the Transformer architecture was introduced in 2017 and the BERT model was introduced in 2019, both by Google, LLMs have revolutionized natural language processing, content generation and beyond. The last few years have seen a surge in the development and deployment of LLMs. As these models grow in capabilities, complexities, and parameter count, a pivotal question emerges for businesses and developers. Should you lean towards open-source models or opt for the closed source alternatives? Open-source models have democratized access to state-of-the-art AI capabilities. Open-source LLMs have publicly accessible source codes which anyone can use, modify, and distribute. This means developers, researchers, and enthusiasts can work together to develop, improve, and customize these models. These open-source models encourage innovation, knowledge sharing, and collective development efforts. Models like Llama 2, Alpaca, and Falcon have carved niches for themselves…
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