The llm_aigc repository is a comprehensive resource for everything related to llm (Large Language Models) and aigc (AI Governance and Control). It provides detailed information, resources, and tools for individuals interested in understanding and working with large language models and AI governance and control. The repository covers a wide range of topics including model training, evaluation, deployment, ethics, and regulations in the AI field.

LLM_book is a learning record and roadmap for programmers with a certain AI foundation to learn Large Language Models (LLM). It covers topics such as PyTorch basics, Transformer architecture, langchain basics, foundational concepts of large models, fine-tuning methods, RAG (Retrieval-Augmented Generation), and building intelligent agents using LLM. The repository provides learning materials, code implementations, and documentation to help users progress in understanding and implementing LLM technologies.

Eino is an ultimate LLM application development framework in Golang, emphasizing simplicity, scalability, reliability, and effectiveness. It provides a curated list of component abstractions, a powerful composition framework, meticulously designed APIs, best practices, and tools covering the entire development cycle. Eino standardizes and improves efficiency in AI application development by offering rich components, powerful orchestration, complete stream processing, highly extensible aspects, and a comprehensive framework structure.

The Alignment Handbook provides robust training recipes for continuing pretraining and aligning language models with human and AI preferences. It includes techniques such as continued pretraining, supervised fine-tuning, reward modeling, rejection sampling, and direct preference optimization (DPO). The handbook aims to fill the gap in public resources on training these models, collecting data, and measuring metrics for optimal downstream performance.

llm_rl is a repository that combines llm (language model) and rl (reinforcement learning) techniques. It likely focuses on using language models in reinforcement learning tasks, such as natural language understanding and generation. The repository may contain implementations of algorithms that leverage both llm and rl to improve performance in various tasks. Developers interested in exploring the intersection of language models and reinforcement learning may find this repository useful for research and experimentation.

Enterprise h2oGPTe - GenAI RAG is a repository containing code examples, notebooks, and benchmarks for the enterprise version of h2oGPTe, a powerful AI tool for generating text based on the RAG (Retrieval-Augmented Generation) architecture. The repository provides resources for leveraging h2oGPTe in enterprise settings, including implementation guides, performance evaluations, and best practices. Users can explore various applications of h2oGPTe in natural language processing tasks, such as text generation, content creation, and conversational AI.

The Atomic Agents framework is a modular and extensible tool designed for creating powerful applications. It leverages Pydantic for data validation and serialization. The framework follows the principles of Atomic Design, providing small and single-purpose components that can be combined. It integrates with Instructor for AI agent architecture and supports various APIs like Cohere, Anthropic, and Gemini. The tool includes documentation, examples, and testing features to ensure smooth development and usage.

MemOS is an operating system for Large Language Models (LLMs) that enhances them with long-term memory capabilities. It allows LLMs to store, retrieve, and manage information, enabling more context-aware, consistent, and personalized interactions. MemOS provides Memory-Augmented Generation (MAG) with a unified API for memory operations, a Modular Memory Architecture (MemCube) for easy integration and management of different memory types, and multiple memory types including Textual Memory, Activation Memory, and Parametric Memory. It is extensible, allowing users to customize memory modules, data sources, and LLM integrations. MemOS demonstrates significant improvements over baseline memory solutions in multiple reasoning tasks, with a notable improvement in temporal reasoning accuracy compared to the OpenAI baseline.

This repository is a curated list of resources for learning and working with Large Language Models (LLMs). It includes a collection of articles, tutorials, tools, datasets, and research papers related to LLMs such as GPT-3, BERT, and Transformer models. Whether you are a researcher, developer, or enthusiast interested in natural language processing and artificial intelligence, this repository provides valuable resources to help you understand, implement, and experiment with LLMs.

llm_related is a repository that documents issues encountered and solutions found during the application of large models. It serves as a knowledge base for troubleshooting and problem-solving in the context of working with complex models in various applications.

MCPHost is a CLI host application that enables Large Language Models (LLMs) to interact with external tools through the Model Context Protocol (MCP). It acts as a host in the MCP client-server architecture, allowing language models to access external tools and data sources, maintain consistent context across interactions, and execute commands safely. The tool supports interactive conversations with Claude 3.5 Sonnet and Ollama models, multiple concurrent MCP servers, dynamic tool discovery and integration, configurable server locations and arguments, and a consistent command interface across model types.

VoltAgent is an open-source TypeScript framework designed for building and orchestrating AI agents. It simplifies the development of AI agent applications by providing modular building blocks, standardized patterns, and abstractions. Whether you're creating chatbots, virtual assistants, automated workflows, or complex multi-agent systems, VoltAgent handles the underlying complexity, allowing developers to focus on defining their agents' capabilities and logic. The framework offers ready-made building blocks, such as the Core Engine, Multi-Agent Systems, Workflow Engine, Extensible Packages, Tooling & Integrations, Data Retrieval & RAG, Memory management, LLM Compatibility, and a Developer Ecosystem. VoltAgent empowers developers to build sophisticated AI applications faster and more reliably, avoiding repetitive setup and the limitations of simpler tools.

Swiftide is a fast, streaming indexing and query library tailored for Retrieval Augmented Generation (RAG) in AI applications. It is built in Rust, utilizing parallel, asynchronous streams for blazingly fast performance. With Swiftide, users can easily build AI applications from idea to production in just a few lines of code. The tool addresses frustrations around performance, stability, and ease of use encountered while working with Python-based tooling. It offers features like fast streaming indexing pipeline, experimental query pipeline, integrations with various platforms, loaders, transformers, chunkers, embedders, and more. Swiftide aims to provide a platform for data indexing and querying to advance the development of automated Large Language Model (LLM) applications.

AI Manus is a general-purpose AI Agent system that supports running various tools and operations in a sandbox environment. It offers deployment with minimal dependencies, supports multiple tools like Terminal, Browser, File, Web Search, and messaging tools, allocates separate sandboxes for tasks, manages session history, supports stopping and interrupting conversations, file upload and download, and is multilingual. The system also provides user login and authentication. The project primarily relies on Docker for development and deployment, with model capability requirements and recommended Deepseek and GPT models.

SolarLLMZeroToAll is a comprehensive repository that provides a step-by-step guide and resources for learning and implementing Solar Longitudinal Learning Machines (SolarLLM) from scratch. The repository covers various aspects of SolarLLM, including theory, implementation, and applications, making it suitable for beginners and advanced users interested in solar energy forecasting and machine learning. The materials include detailed explanations, code examples, datasets, and visualization tools to facilitate understanding and practical implementation of SolarLLM models.

MCP Context Forge is a powerful tool for generating context-aware data for machine learning models. It provides functionalities to create diverse datasets with contextual information, enhancing the performance of AI algorithms. The tool supports various data formats and allows users to customize the context generation process easily. With MCP Context Forge, users can efficiently prepare training data for tasks requiring contextual understanding, such as sentiment analysis, recommendation systems, and natural language processing.

The Azure-Analytics-and-AI-Engagement repository provides packaged Industry Scenario DREAM Demos with ARM templates (Containing a demo web application, Power BI reports, Synapse resources, AML Notebooks etc.) that can be deployed in a customer’s subscription using the CAPE tool within a matter of few hours. Partners can also deploy DREAM Demos in their own subscriptions using DPoC.

Sorrentum is an open-source project that aims to combine open-source development, startups, and brilliant students to build machine learning, AI, and Web3 / DeFi protocols geared towards finance and economics. The project provides opportunities for internships, research assistantships, and development grants, as well as the chance to work on cutting-edge problems, learn about startups, write academic papers, and get internships and full-time positions at companies working on Sorrentum applications.

TiDB is an open-source distributed SQL database that supports Hybrid Transactional and Analytical Processing (HTAP) workloads. It is MySQL compatible and features horizontal scalability, strong consistency, and high availability.

Zep is an open-source platform for building and deploying large language model (LLM) applications. It provides a suite of tools and services that make it easy to integrate LLMs into your applications, including chat history memory, embedding, vector search, and data enrichment. Zep is designed to be scalable, reliable, and easy to use, making it a great choice for developers who want to build LLM-powered applications quickly and easily.

This repository codifies the Airflow cluster that is deployed at workflow.telemetry.mozilla.org (behind SSO) and commonly referred to as "WTMO" or simply "Airflow". Some links relevant to users and developers of WTMO: * The `dags` directory in this repository contains some custom DAG definitions * Many of the DAGs registered with WTMO don't live in this repository, but are instead generated from ETL task definitions in bigquery-etl * The Data SRE team maintains a WTMO Developer Guide (behind SSO)

Mojo is a new programming language that bridges the gap between research and production by combining Python syntax and ecosystem with systems programming and metaprogramming features. Mojo is still young, but it is designed to become a superset of Python over time.

PandasAI is a Python library that makes it easy to ask questions to your data in natural language. It helps you to explore, clean, and analyze your data using generative AI.

Databend is an open-source cloud data warehouse that serves as a cost-effective alternative to Snowflake. With its focus on fast query execution and data ingestion, it's designed for complex analysis of the world's largest datasets.

This repository is dedicated to summarizing papers related to large language models with the field of law. It includes applications of large language models in legal tasks, legal agents, legal problems of large language models, data resources for large language models in law, law LLMs, and evaluation of large language models in the legal domain.

This repository is a comprehensive guide for individuals looking to start and improve their skills in Large Language Models (LLMs) without an advanced background in the field. It provides free resources, online courses, books, articles, and practical tips to become an expert in machine learning. The guide covers topics such as terminology, transformers, prompting, retrieval augmented generation (RAG), and more. It also includes recommendations for podcasts, YouTube videos, and communities to stay updated with the latest news in AI and LLMs.

AI Verify is an AI governance testing framework and software toolkit that validates the performance of AI systems against internationally recognised principles through standardised tests. It offers a new API Connector feature to bypass size limitations, test various AI frameworks, and configure connection settings for batch requests. The toolkit operates within an enterprise environment, conducting technical tests on common supervised learning models for tabular and image datasets. It does not define AI ethical standards or guarantee complete safety from risks or biases.

This repository contains a collection of research papers related to watermarking techniques for text and images, specifically focusing on large language models (LLMs). The papers cover various aspects of watermarking LLM-generated content, including robustness, statistical understanding, topic-based watermarks, quality-detection trade-offs, dual watermarks, watermark collision, and more. Researchers have explored different methods and frameworks for watermarking LLMs to protect intellectual property, detect machine-generated text, improve generation quality, and evaluate watermarking techniques. The repository serves as a valuable resource for those interested in the field of watermarking for LLMs.

This repository contains code for reproducing experiments on lie detection in black-box LLMs by asking unrelated questions. It includes Q/A datasets, prompts, and fine-tuning datasets for generating lies with language models. The lie detectors rely on asking binary 'elicitation questions' to diagnose whether the model has lied. The code covers generating lies from language models, training and testing lie detectors, and generalization experiments. It requires access to GPUs and OpenAI API calls for running experiments with open-source models. Results are stored in the repository for reproducibility.

The GraphRAG project is a data pipeline and transformation suite designed to extract meaningful, structured data from unstructured text using LLMs. It enhances LLMs' ability to reason about private data. The repository provides guidance on using knowledge graph memory structures to enhance LLM outputs, with a warning about the potential costs of GraphRAG indexing. It offers contribution guidelines, development resources, and encourages prompt tuning for optimal results. The Responsible AI FAQ addresses GraphRAG's capabilities, intended uses, evaluation metrics, limitations, and operational factors for effective and responsible use.

LangTest is a comprehensive evaluation library for custom LLM and NLP models. It aims to deliver safe and effective language models by providing tools to test model quality, augment training data, and support popular NLP frameworks. LangTest comes with benchmark datasets to challenge and enhance language models, ensuring peak performance in various linguistic tasks. The tool offers more than 60 distinct types of tests with just one line of code, covering aspects like robustness, bias, representation, fairness, and accuracy. It supports testing LLMS for question answering, toxicity, clinical tests, legal support, factuality, sycophancy, and summarization.

Awesome-Jailbreak-on-LLMs is a collection of state-of-the-art, novel, and exciting jailbreak methods on Large Language Models (LLMs). The repository contains papers, codes, datasets, evaluations, and analyses related to jailbreak attacks on LLMs. It serves as a comprehensive resource for researchers and practitioners interested in exploring various jailbreak techniques and defenses in the context of LLMs. Contributions such as additional jailbreak-related content, pull requests, and issue reports are welcome, and contributors are acknowledged. For any inquiries or issues, contact [email protected]. If you find this repository useful for your research or work, consider starring it to show appreciation.