Creating and deploying your own Text Generation Interface (TGI) API using open-source Large Language Models (LLMs) on AWS presents a compelling alternative to proprietary models, offering enhanced privacy, security, and flexibility. This article delves into the latest advancements in open-source LLMs and their deployment on AWS, focusing on the Hugging Face LLM Inference containers and the Falcon 180B model.
The Rise of Open-Source LLMs
Open-source LLMs, like Llama 2, PaLM 2, GPT-NeoX-20B, and Falcon 180B, have become increasingly popular due to their cost-effectiveness and high performance. Llama 2, for example, offers a range of models with parameters spanning from 7 to 70 billion, showing performance comparable to closed-source models like ChatGPT and PaLM. Falcon 180B, developed by the Technology Innovation Institute (TII) and trained on Amazon SageMaker, is available for deployment through Amazon SageMaker JumpStart. It boasts 180 billion parameters and is trained on a massive 3.5 trillion-token dataset, making it one of the most performant open-source models available.
