Unoplat-CodeConfluence is a universal code context engine that aims to extract, understand, and provide precise code context across repositories tied through domains. It combines deterministic code grammar with state-of-the-art LLM pipelines to achieve human-like understanding of codebases in minutes. The tool offers smart summarization, graph-based embedding, enhanced onboarding, graph-based intelligence, deep dependency insights, and seamless integration with existing development tools and workflows. It provides a precise context API for knowledge engine and AI coding assistants, enabling reliable code understanding through bottom-up code summarization, graph-based querying, and deep package and dependency analysis.

The Model Context Protocol (MCP) Curriculum for Beginners is an open-source framework designed to standardize interactions between AI models and client applications. It offers a structured learning path with practical coding examples and real-world use cases in popular programming languages like C#, Java, JavaScript, Rust, Python, and TypeScript. Whether you're an AI developer, system architect, or software engineer, this guide provides comprehensive resources for mastering MCP fundamentals and implementation strategies.

AI-For-Beginners is a comprehensive 12-week, 24-lesson curriculum designed by experts at Microsoft to introduce beginners to the world of Artificial Intelligence (AI). The curriculum covers various topics such as Symbolic AI, Neural Networks, Computer Vision, Natural Language Processing, Genetic Algorithms, and Multi-Agent Systems. It includes hands-on lessons, quizzes, and labs using popular frameworks like TensorFlow and PyTorch. The focus is on providing a foundational understanding of AI concepts and principles, making it an ideal starting point for individuals interested in AI.

COLD-Attack is a framework designed for controllable jailbreaks on large language models (LLMs). It formulates the controllable attack generation problem and utilizes the Energy-based Constrained Decoding with Langevin Dynamics (COLD) algorithm to automate the search of adversarial LLM attacks with control over fluency, stealthiness, sentiment, and left-right-coherence. The framework includes steps for energy function formulation, Langevin dynamics sampling, and decoding process to generate discrete text attacks. It offers diverse jailbreak scenarios such as fluent suffix attacks, paraphrase attacks, and attacks with left-right-coherence.

A curated repository of code recipes, demos, and resources for basic and advanced Redis use cases in the AI ecosystem. It includes demos for ArxivChatGuru, Redis VSS, Vertex AI & Redis, Agentic RAG, ArXiv Search, and Product Search. Recipes cover topics like Getting started with RAG, Semantic Cache, Advanced RAG, and Recommendation systems. The repository also provides integrations/tools like RedisVL, AWS Bedrock, LangChain Python, LangChain JS, LlamaIndex, Semantic Kernel, RelevanceAI, and DocArray. Additional content includes blog posts, talks, reviews, and documentation related to Vector Similarity Search, AI-Powered Document Search, Vector Databases, Real-Time Product Recommendations, and more. Benchmarks compare Redis against other Vector Databases and ANN benchmarks. Documentation includes QuickStart guides, official literature for Vector Similarity Search, Redis-py client library docs, Redis Stack documentation, and Redis client list.

A curated list of 50+ resources to help you become a Generative AI Data Scientist. This repository includes resources on building GenAI applications with Large Language Models (LLMs), and deploying LLMs and GenAI with Cloud-based solutions.

TrustLLM is a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets. The document explains how to use the trustllm python package to help you assess the performance of your LLM in trustworthiness more quickly. For more details about TrustLLM, please refer to project website.

Rubra is a collection of open-weight large language models enhanced with tool-calling capability. It allows users to call user-defined external tools in a deterministic manner while reasoning and chatting, making it ideal for agentic use cases. The models are further post-trained to teach instruct-tuned models new skills and mitigate catastrophic forgetting. Rubra extends popular inferencing projects for easy use, enabling users to run the models easily.

PredictorLLM is an advanced trading agent framework that utilizes large language models to automate trading in financial markets. It includes a profiling module to establish agent characteristics, a layered memory module for retaining and prioritizing financial data, and a decision-making module to convert insights into trading strategies. The framework mimics professional traders' behavior, surpassing human limitations in data processing and continuously evolving to adapt to market conditions for superior investment outcomes.

Awesome-LLM-Large-Language-Models-Notes is a repository that provides a comprehensive collection of information on various Large Language Models (LLMs) classified by year, size, and name. It includes details on known LLM models, their papers, implementations, and specific characteristics. The repository also covers LLM models classified by architecture, must-read papers, blog articles, tutorials, and implementations from scratch. It serves as a valuable resource for individuals interested in understanding and working with LLMs in the field of Natural Language Processing (NLP).

HuatuoGPT2 is an innovative domain-adapted medical large language model that excels in medical knowledge and dialogue proficiency. It showcases state-of-the-art performance in various medical benchmarks, surpassing GPT-4 in expert evaluations and fresh medical licensing exams. The open-source release includes HuatuoGPT2 models in 7B, 13B, and 34B versions, training code for one-stage adaptation, partial pre-training and fine-tuning instructions, and evaluation methods for medical response capabilities and professional pharmacist exams. The tool aims to enhance LLM capabilities in the Chinese medical field through open-source principles.

Comet Opik is a repository containing two main services: a frontend and a backend. It provides a Python SDK for easy installation. Users can run the full application locally with minikube, following specific installation prerequisites. The repository structure includes directories for applications like Opik backend, with detailed instructions available in the README files. Users can manage the installation using simple k8s commands and interact with the application via URLs for checking the running application and API documentation. The repository aims to facilitate local development and testing of Opik using Kubernetes technology.

This repository provides code for extracting image and text features using OpenAI CLIP models, supporting both global and local grid visual features. It aims to facilitate multi visual-and-language downstream tasks by allowing users to customize input and output grid resolution easily. The extracted features have shown comparable or superior results in image captioning tasks without hyperparameter tuning. The repo supports various CLIP models and provides detailed information on supported settings and results on MSCOCO image captioning. Users can get started by setting up experiments with the extracted features using X-modaler.

This repository contains the source code for the SIGIR 2023 paper 'Where to Go Next for Recommender Systems? ID- vs. Modality-based Recommender Models Revisited'. It provides resources for evaluating foundation, transferable, multi-modal, and LLM recommendation models, along with datasets, pre-trained models, and training strategies for IDRec and MoRec using in-batch debiased cross-entropy loss. The repository also offers large-scale datasets, code for SASRec with in-batch debias cross-entropy loss, and information on joining the lab for research opportunities.

This project is a c++ version of the AI reasoning library that supports the reasoning of tensorrt models. It provides accelerated deployment cases of deep learning CV popular models and supports dynamic-batch image processing, inference, decode, and NMS. The project has been updated with various models and provides tutorials for model exports. It also includes a producer-consumer inference model for specific tasks. The project directory includes implementations for model inference applications, backend reasoning classes, post-processing, pre-processing, and target detection and tracking. Speed tests have been conducted on various models, and onnx downloads are available for different models.

This repository contains the complete archive of the 2024 version of the 'Intelligent Computing System' experiment at the University of Chinese Academy of Sciences. The experiment content for 2024 has undergone extensive adjustments to the knowledge system and experimental topics, including the transition from TensorFlow to PyTorch, significant modifications to previous code, and the addition of experiments with large models. The project is continuously updated in line with the course progress, currently up to the seventh experiment. Updates include the addition of experiments like YOLOv5 in Experiment 5-3, updates to theoretical teaching materials, and fixes for bugs in Experiment 6 code. The repository also includes experiment manuals, questions, and answers for various experiments, with some data sets hosted on Baidu Cloud due to size limitations on GitHub.

Unoplat-CodeConfluence is a universal code context engine that aims to extract, understand, and provide precise code context across repositories tied through domains. It combines deterministic code grammar with state-of-the-art LLM pipelines to achieve human-like understanding of codebases in minutes. The tool offers smart summarization, graph-based embedding, enhanced onboarding, graph-based intelligence, deep dependency insights, and seamless integration with existing development tools and workflows. It provides a precise context API for knowledge engine and AI coding assistants, enabling reliable code understanding through bottom-up code summarization, graph-based querying, and deep package and dependency analysis.

Gemini CLI is an open-source AI agent that provides lightweight access to Gemini, offering powerful capabilities like code understanding, generation, automation, integration, and advanced features. It is designed for developers who prefer working in the command line and offers extensibility through MCP support. The tool integrates directly into GitHub workflows and offers various authentication options for individual developers, enterprise teams, and production workloads. With features like code querying, editing, app generation, debugging, and GitHub integration, Gemini CLI aims to streamline development workflows and enhance productivity.

CodeTF is a Python transformer-based library for code large language models (Code LLMs) and code intelligence. It provides an interface for training and inferencing on tasks like code summarization, translation, and generation. The library offers utilities for code manipulation across various languages, including easy extraction of code attributes. Using tree-sitter as its core AST parser, CodeTF enables parsing of function names, comments, and variable names. It supports fast model serving, fine-tuning of LLMs, various code intelligence tasks, preprocessed datasets, model evaluation, pretrained and fine-tuned models, and utilities to manipulate source code. CodeTF aims to facilitate the integration of state-of-the-art Code LLMs into real-world applications, ensuring a user-friendly environment for code intelligence tasks.

Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.

The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.