![]() Hosting LLMs such as Falcon-40B and Falcon-7B can be challenging. SageMaker large model inference DLCs simplify LLM hosting This post will help you get started with deploying Falcon on SageMaker for high-accuracy inference in these types of domains. The model is available in two different sizes-Falcon-40B and Falcon-7B-and can be used for state-of-the-art performance in applications such as language understanding, conversational experiences, and automated writing assistance. Trained on 1 trillion tokens, Falcon boasts top-notch performance against the Eleuther AI Language Model Evaluation Harness and is currently #1 on the Hugging Face leaderboard for accuracy. The result of this effort is TII Falcon LLM. Earlier this year, TII set out to train a state-of-the-art, open-source LLM and used the infrastructure, tooling, and expertise of SageMaker to get the job done (to learn more about how this model was trained on SageMaker, refer to Technology Innovation Institute trains the state-of-the-art Falcon LLM 40B foundation model on Amazon SageMaker). TII is the applied research organization within Abu Dhabi’s Advanced Technology Research Council its team of scientists, researchers, and engineers is dedicated to the discovery of transformative technologies and development of scientific breakthroughs that will future-proof our society. ![]() In this post, we demonstrate how to deploy Falcon for applications like language understanding and automated writing assistance using large model inference deep learning containers on SageMaker. Trained on 1 trillion tokens with Amazon SageMaker, Falcon boasts top-notch performance (#1 on the Hugging Face leaderboard at time of writing) while being comparatively lightweight and less expensive to host than other LLMs such as llama-65B. Last week, Technology Innovation Institute (TII) launched TII Falcon LLM, an open-source foundational large language model (LLM).
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