We introduce LayoutLM, one of the renowned models for extracting information from documents, developed by Microsoft. To tailor a solution for our specific needs, we label our documents using Label Studio, an open-source labeling tool, connected to our remote storage AWS S3.
free and paid courses available for learning about Artificial Intelligence (AI). It mentions courses from NVIDIA, Google Cloud Skills Boost program, Amazon's training center, OpenAI's partner DeepLearning.ai, edX, and Microsoft.
This tutorial introduces promptrefiner, a tool created by Amirarsalan Rajabi that uses the GPT-4 model to create perfect system prompts for local LLMs.
This GitHub repository contains a course on Large Language Models (LLMs) with roadmaps and Colab notebooks. The course is divided into three parts: LLM Fundamentals, The LLM Scientist, and The LLM Engineer. Each part covers various topics, including mathematics, Python, neural networks, instruction datasets, pre-training, supervised fine-tuning, reinforcement learning from human feedback, evaluation, quantization, new trends, running LLMs, building a vector storage, retrieval augmented generation, advanced RAG, inference optimization, and deployment.
This article explores how to boost the performance of small language models by using supervision from larger ones through knowledge distillation. The article provides a step-by-step guide on how to distill knowledge from a teacher model (LLama 2–70B) to a student model (Tiny-LLama) using unlabeled in-domain data and targeted prompting.
An in-depth guide about Mistral 7B, a 7-billion-parameter language model released by Mistral AI. This guide includes an introduction to the model, its capabilities, code generation, limitations, guardrails, and enforcing guardrails. It also covers applications, papers, and additional reading materials related to Mistral 7B and finetuned models.
This article explores the application of XML Schema in AI systems and prompts. XML Schema provides a structured way to describe and validate data, making it an essential tool for AI systems that deal with data. The author discusses how XML Schema can be used to create and manage data in AI applications, such as speech recognition and natural language processing. The article also covers the benefits of using XML Schema in AI systems, including improved data consistency, interoperability, and security. Lastly, the author provides some examples of XML Schema usage in AI systems and discusses the future of XML Schema in AI technology.
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