Information extraction with Mistral 8x7B LLM
Introduction
In the contemporary landscape dominated by information, the retrieval of data from unstructured sources, notably PDF documents, has become an indispensable task for a diverse array of stakeholders, spanning businesses, researchers, and individuals alike. Traditional manual extraction methods, once the conventional practice, are now perceived as both labor-intensive and susceptible to errors, highlighting the imperative for more efficient and accurate approaches. This blog extensively explores the dynamic sphere of information extraction, harnessing the prowess of Large Language Models. It revolves around the transformative applications of these models in the domain of processing and scrutinizing PDF files.
We attempt to extract information using the Mistral 8x7B model in this blog.
Motivation
Within the ever-evolving realm of Natural Language Processing (NLP), achieving optimal model performance frequently coincides with the drawback of escalated model dimensions. This, in turn, results in heightened computational costs and inference latency, posing obstacles to the seamless integration of NLP models into real-world applications. Consequently, the quest for models that achieve a harmonious equilibrium between superior performance and operational efficiency stands as a crucial imperative.
In the realm of Mistral AI’s innovative models, Mixtral 8x7B emerges as a powerful Sparse Mixture of Experts (SMoE)…
