Coding-Tutor

This repository explores the potential of LLMs as coding tutors through the proposed Traver agent workflow. It focuses on incorporating knowledge tracing and turn-by-turn verification to tackle challenges in coding tutoring. The method extends beyond coding to other task-tutoring scenarios, adapting content to users' varying levels of background knowledge. The repository introduces the DICT evaluation protocol for assessing tutor performance through student simulation and coding tests. It also discusses the inference-time scaling with verifiers and provides resources for training and evaluation.

agentUniverse is a multi-agent framework based on large language models, providing flexible capabilities for building individual agents. It focuses on multi-agent collaborative patterns, integrating domain experience to help agents solve problems in various fields. The framework includes pattern components like PEER and DOE for event interpretation, industry analysis, and financial report generation. It offers features for agent construction, multi-agent collaboration, and domain expertise integration, aiming to create intelligent applications with professional know-how.

agentUniverse is a multi-agent framework based on large language models, providing flexible capabilities for building individual agents. It focuses on collaborative pattern components to solve problems in various fields and integrates domain experience. The framework supports LLM model integration and offers various pattern components like PEER and DOE. Users can easily configure models and set up agents for tasks. agentUniverse aims to assist developers and enterprises in constructing domain-expert-level intelligent agents for seamless collaboration.

CodeFuse-muAgent is a Multi-Agent framework designed to streamline Standard Operating Procedure (SOP) orchestration for agents. It integrates toolkits, code libraries, knowledge bases, and sandbox environments for rapid construction of complex Multi-Agent interactive applications. The framework enables efficient execution and handling of multi-layered and multi-dimensional tasks.

OpsPilot is an AI-powered operations navigator developed by the WeOps team. It leverages deep learning and LLM technologies to make operations plans interactive and generalize and reason about local operations knowledge. OpsPilot can be integrated with web applications in the form of a chatbot and primarily provides the following capabilities: 1. Operations capability precipitation: By depositing operations knowledge, operations skills, and troubleshooting actions, when solving problems, it acts as a navigator and guides users to solve operations problems through dialogue. 2. Local knowledge Q&A: By indexing local knowledge and Internet knowledge and combining the capabilities of LLM, it answers users' various operations questions. 3. LLM chat: When the problem is beyond the scope of OpsPilot's ability to handle, it uses LLM's capabilities to solve various long-tail problems.

Agents 2.0 is a framework for training language agents using symbolic learning, inspired by connectionist learning for neural nets. It implements main components of connectionist learning like back-propagation and gradient-based weight update in the context of agent training using language-based loss, gradients, and weights. The framework supports optimizing multi-agent systems and allows multiple agents to take actions in one node.

Me LLaMA introduces a suite of open-source medical Large Language Models (LLMs), including Me LLaMA 13B/70B and their chat-enhanced versions. Developed through innovative continual pre-training and instruction tuning, these models leverage a vast medical corpus comprising PubMed papers, medical guidelines, and general domain data. Me LLaMA sets new benchmarks on medical reasoning tasks, making it a significant asset for medical NLP applications and research. The models are intended for computational linguistics and medical research, not for clinical decision-making without validation and regulatory approval.

KAG is a logical reasoning and Q&A framework based on the OpenSPG engine and large language models. It is used to build logical reasoning and Q&A solutions for vertical domain knowledge bases. KAG supports logical reasoning, multi-hop fact Q&A, and integrates knowledge and chunk mutual indexing structure, conceptual semantic reasoning, schema-constrained knowledge construction, and logical form-guided hybrid reasoning and retrieval. The framework includes kg-builder for knowledge representation and kg-solver for logical symbol-guided hybrid solving and reasoning engine. KAG aims to enhance LLM service framework in professional domains by integrating logical and factual characteristics of KGs.

agentUniverse is a framework for developing applications powered by multi-agent based on large language model. It provides essential components for building single agent and multi-agent collaboration mechanism for customizing collaboration patterns. Developers can easily construct multi-agent applications and share pattern practices from different fields. The framework includes pre-installed collaboration patterns like PEER and DOE for complex task breakdown and data-intensive tasks.

Aligner is a model-agnostic alignment tool designed to efficiently correct responses from large language models. It redistributes initial answers to align with human intentions, improving performance across various LLMs. The tool can be applied with minimal training, enhancing upstream models and reducing hallucination. Aligner's 'copy and correct' method preserves the base structure while enhancing responses. It achieves significant performance improvements in helpfulness, harmlessness, and honesty dimensions, with notable success in boosting Win Rates on evaluation leaderboards.

Nucleoid is a declarative (logic) runtime environment that manages both data and logic under the same runtime. It uses a declarative programming paradigm, which allows developers to focus on the business logic of the application, while the runtime manages the technical details. This allows for faster development and reduces the amount of code that needs to be written. Additionally, the sharding feature can help to distribute the load across multiple instances, which can further improve the performance of the system.

Kaapana is an open-source toolkit for state-of-the-art platform provisioning in the field of medical data analysis. The applications comprise AI-based workflows and federated learning scenarios with a focus on radiological and radiotherapeutic imaging. Obtaining large amounts of medical data necessary for developing and training modern machine learning methods is an extremely challenging effort that often fails in a multi-center setting, e.g. due to technical, organizational and legal hurdles. A federated approach where the data remains under the authority of the individual institutions and is only processed on-site is, in contrast, a promising approach ideally suited to overcome these difficulties. Following this federated concept, the goal of Kaapana is to provide a framework and a set of tools for sharing data processing algorithms, for standardized workflow design and execution as well as for performing distributed method development. This will facilitate data analysis in a compliant way enabling researchers and clinicians to perform large-scale multi-center studies. By adhering to established standards and by adopting widely used open technologies for private cloud development and containerized data processing, Kaapana integrates seamlessly with the existing clinical IT infrastructure, such as the Picture Archiving and Communication System (PACS), and ensures modularity and easy extensibility.

Parrot is a distributed serving system for LLM-based Applications, designed to efficiently serve LLM-based applications by adding Semantic Variable in the OpenAI-style API. It allows for horizontal scalability with multiple Engine instances running LLM models communicating with ServeCore. The system enables AI agents to interact with LLMs via natural language prompts for collaborative tasks.

PARAM Benchmarks is a repository of communication and compute micro-benchmarks as well as full workloads for evaluating training and inference platforms. It complements commonly used benchmarks by focusing on AI training with PyTorch based collective benchmarks, GEMM, embedding lookup, linear layer, and DLRM communication patterns. The tool bridges the gap between stand-alone C++ benchmarks and PyTorch/Tensorflow based application benchmarks, providing deep insights into system architecture and framework-level overheads.

LLM Hallucination Index - RAG Special is a comprehensive evaluation of large language models (LLMs) focusing on context length and open vs. closed-source attributes. The index explores the impact of context length on model performance and tests the assumption that closed-source LLMs outperform open-source ones. It also investigates the effectiveness of prompting techniques like Chain-of-Note across different context lengths. The evaluation includes 22 models from various brands, analyzing major trends and declaring overall winners based on short, medium, and long context insights. Methodologies involve rigorous testing with different context lengths and prompting techniques to assess models' abilities in handling extensive texts and detecting hallucinations.

This repository explores the potential of LLMs as coding tutors through the proposed Traver agent workflow. It focuses on incorporating knowledge tracing and turn-by-turn verification to tackle challenges in coding tutoring. The method extends beyond coding to other task-tutoring scenarios, adapting content to users' varying levels of background knowledge. The repository introduces the DICT evaluation protocol for assessing tutor performance through student simulation and coding tests. It also discusses the inference-time scaling with verifiers and provides resources for training and evaluation.

The 'awesome-ai-llm4education' repository is a curated list of papers related to artificial intelligence (AI) and large language models (LLM) for education. It collects papers from top conferences, journals, and specialized domain-specific conferences, categorizing them based on specific tasks for better organization. The repository covers a wide range of topics including tutoring, personalized learning, assessment, material preparation, specific scenarios like computer science, language, math, and medicine, aided teaching, as well as datasets and benchmarks for educational research.

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.