llm

Emerging task automation and AI agents with GPT-4 after LangChain and LlamaIndex integration trend Link

Building LLM applications for production Link

Large Language Models (LLMs) like OpenAI ChatGPT are called foundational models because even though they are trained for a relatively small set of tasks, they work exceptionally well for multiple unseen downstream tasks. While there is still some debate on how they are so good, at a high level it is quite easy to under what they do — they just predict the next word (read tokens). And all the cool tools you see built using these models, are nothing but the smart application of this feature.

Finance NLP is a John Snow Lab’s product, launched 2022 to provide state-of-the-art, autoscalable, domain-specific NLP on top of Spark. With more than 100 models, featuring Deep Learning and Transformer-based architectures Link

A guide on when to use small and large language models Link

Learn how to develop a LangChain agent that has multiple ways of interacting with the Neo4j database Link

In this guide, I share my analysis of the current architectural best practices for data-informed language model applications. This particular subdiscipline is experiencing phenomenal research interest even by the standards of large language models - in this guide, I cite 8 research papers and 4 software projects, with a median initial publication date of November 22nd, 2022. Link

The purpose of this repository is to let people to use lots of open sourced instruction-following fine-tuned LLM models as a Chatbot service. Because different models behave differently, and different models require differently formmated prompts, I made a very simple library Ping Pong for model agnostic conversation and context managements. Link

PDFGPT.IO Link

Semantra is a multipurpose tool for semantically searching documents. Query by meaning rather than just by matching text. Link

Text2Diagram: Create schematic architecture diagrams, UML, flowcharts, Gnatt charts, mind maps, Sequence diagrams, User Journey and many more with natural language. Link

A curated (still actively updated) list of practical guide resources of LLMs. It’s based on our survey paper: Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond and efforts from @xinyadu. The survey is partially based on the second half of this Blog. We also build an evolutionary tree of modern Large Language Models (LLMs) to trace the development of language models in recent years and highlights some of the most well-known models.

At present, our core contributors are preparing the 65B version and we expect to empower WizardLM with the ability to perform instruction evolution itself, aiming to evolve your specific data at a low cost. Link

Over the past two weeks, there has been a massive increase in using LLMs in an agentic manner. Specifically, projects like AutoGPT, BabyAGI, CAMEL, and Generative Agents have popped up. The LangChain community has now implemented some parts of all of those projects in the LangChain framework. While researching and implementing these projects, we’ve tried to best understand what the differences between them are and what the novel features of each are.

Chat with your CSV files with a memory chatbot🤖 | Made with Langchain🦜 and OpenAI🧠 Link