A large language model (LLM) is a type of machine learning model designed for understanding, generating, and interacting with human language. These models are trained on extensive datasets containing text from books, articles, websites, and other sources to learn patterns, context, and semantics in language. LLMs are widely used in applications like chatbots, code generation, translation, summarization, and more. They are often built using transformer architectures and are central to the field of generative AI.
Practical Llama 3 inference in Java
AI Tasks. A LLM integrated workflow orchestration tool for Liferay.
An android chatbot that integrates open ai API for GPT 3.5 .This chatbot also has exception handling handling for network errors as we tend to face lots of network problems in Africa
🔎📚 This document processing system is designed to efficiently analyze user documents and provide accurate responses to user queries related to the content. Powered by advanced algorithms, it offers a seamless experience for users seeking insights or information within their documents.
Examples for using Ollama4j to connect and interact with Ollama models.
LLMs' performance analysis on CPU, GPU, Execution Time and Energy Usage
Demonstrate two types of chat interactions with a Mattermost instance leveraging Mattermost OpenAPI v3 spec and Spring AI.
PoC for the acceleration of the core Math functions in Llama2.c to run on GPUs with Shared Memory
API Rest da aplicação AluMind
Community Spring Boot and Spring AI building blocks for conversations between humans and artificial intelligence.
A university project concerning the use of RAG(Retrieval Augmented Generation) technique and Spring framework to implement a simple financial advisor agent.