This is a local LLM chatbot project with RAG for processing PDF input files
In this article, Dr. Leon Eversberg explains how to build an advanced Local Language Model (LLM) Retrieval-Augmented Generation (RAG) pipeline using open-source bi-encoders and cross-encoders for better chatbot performance.
This article explains what temperature is in the context of language models, how it works, its relationship to beam search, and how output generation can still go haywire despite these techniques.
The Towards Data Science team highlights recent articles on the rise of open-source LLMs, ethical considerations with chatbots, potential manipulation of LLM recommendations, and techniques for temperature scaling and re-ranking in generative AI.
Google's Gemini Pro model in NotebookLM can now create study guides, FAQs, quizzes, and even spoken dialogue discussions. This new feature allows students to learn in an interactive and personalized way by connecting physics and basketball through AI-generated examples.
MIT CSAIL researchers have developed three neurosymbolic frameworks - LILO, Ada, and LGA - that use natural language to help large language models (LLMs) build better abstractions for coding, AI planning, and robotics tasks.
Podman AI Lab is an open source extension for Podman Desktop that allows users to work with LLMs on a local environment, featuring a recipe catalog with common AI use cases, a curated set of open source models, and a playground for learning, prototyping, and experimentation. It uses Podman machines to run inference servers for LLM models and supports various formats like GGUF, Pytorch, and Tensorflow.
A blog post discussing the use of Llamafiles for embeddings in Retrieval-Augmented Generation (RAG) applications and recommending the best models based on performance on RAG-relevant tasks.
This article discusses causal inference, an emerging field in machine learning that goes beyond predicting what could happen to focus on understanding the cause-and-effect relationships in data. The author explains how to detect and fix errors in a directed acyclic graph (DAG) to make it a valid representation of the underlying data.
