Finetuning LLM to Search & Generate New Patents

The blog post details the development of a custom application named PatentPT, which leverages fine-tuned large language models (LLMs) to enhance patent search and retrieval processes. In response to the need for more practical guides on deploying LLMs on custom text corpuses, the authors describe how they domain-trained and fine-tuned an LLM using the vast United States Patent and Trademark Office (USPTO) dataset. The application, PatentPT, improves upon traditional patent search methods by providing features such as autocomplete, abstract and claim generation, and general chat functionalities. The technical architecture involves creating an ensemble of fine-tuned LLMs and custom search indices, utilizing tools like Habana Gaudi hardware, Deep Lake databases for AI, and Hugging Face's Optimum library to achieve efficient training and deployment. The result is a scalable, LLM-powered application that demonstrates greater accuracy and control over output than general AI APIs, showcasing the integration of cutting-edge technologies in the field of large language modeling.

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