#In-Context Learning

LMOps is a research initiative dedicated to developing AI solutions with foundation models, focusing on enhancing capabilities through Large Language Models (LLMs) and Generative AI. It explores areas such as automatic prompt optimization, extensible prompts, structured prompting, and LLM inference acceleration. The project addresses technologies like context demonstration selection and instruction tuning, improving the functionality and customizability of AI solutions across domains. By providing insights into in-context learning, LMOps significantly contributes to the advancement of AI technologies.

The open-source guide offers resources for in-context learning and prompt engineering, assisting in mastering large language models (LLMs) such as ChatGPT and GPT3. The repository contains recent research papers, an experimentation playground for LLMs, and practical prompt engineering techniques. It features prompts for workplace and daily use, along with a LangChain-supported LLM usage guide. Regular updates aim to prepare users for advancing toward Artificial General Intelligence, optimizing skills in the face of automation.

This repository provides a diverse selection of papers and materials aimed at exploring reasoning skills in large language models such as GPT-3. It delves into techniques like fully supervised finetuning and prompting for better understanding and implementing logical reasoning in NLP frameworks. Surveys and position papers offer thorough insights and analyses, encouraging further exploration of reasoning strategies. Researchers and developers are invited to enhance this collection by sharing additional papers, supporting a collaborative effort to advance NLP models.

Discover a comprehensive collection of tool learning and AI resources, featuring surveys, fine-tuning methods, and in-context learning improvements. Uncover practical applications like Auto-GPT and LangChain. Regular updates make it a valuable resource for developers and researchers focused on advancing AI tool integration.