Academic Forge is a collection of skills integrated for academic writing workflows. It provides a curated set of skills related to academic writing and research, allowing for precise skill calls, avoiding confusion between similar skills, maintaining focus on research workflows, and receiving timely updates from original authors. The forge integrates carefully selected skills covering various areas such as bioinformatics, clinical research, data analysis, scientific writing, laboratory automation, machine learning, databases, AI research, model architectures, fine-tuning, post-training, distributed training, optimization, inference, evaluation, agents, multimodal tasks, and machine learning paper writing. It is designed to streamline the academic writing and AI research processes by providing a cohesive and community-driven collection of skills.

Daily-DeepLearning is a repository that covers various computer science topics such as data structures, operating systems, computer networks, Python programming, data science packages like numpy, pandas, matplotlib, machine learning theories, deep learning theories, NLP concepts, machine learning practical applications, deep learning practical applications, and big data technologies like Hadoop and Hive. It also includes coding exercises related to '剑指offer'. The repository provides detailed explanations and examples for each topic, making it a comprehensive resource for learning and practicing different aspects of computer science and data-related fields.

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.

ChatWiki is an open-source knowledge base AI question-answering system. It is built on large language models (LLM) and retrieval-augmented generation (RAG) technologies, providing out-of-the-box data processing, model invocation capabilities, and helping enterprises quickly build their own knowledge base AI question-answering systems. It offers exclusive AI question-answering system, easy integration of models, data preprocessing, simple user interface design, and adaptability to different business scenarios.

KCORES LLM Arena is a large model evaluation tool that focuses on real-world scenarios, using human scoring and benchmark testing to assess performance. It aims to provide an unbiased evaluation of large models in real-world applications. The tool includes programming ability tests and specific benchmarks like Mandelbrot Set, Mars Mission, Solar System, and Ball Bouncing Inside Spinning Heptagon. It supports various programming languages and emphasizes performance optimization, rendering, animations, physics simulations, and creative implementations.

This repository provides a comprehensive collection of resources, open-source tools, and knowledge related to quantitative analysis. It serves as a valuable knowledge base and navigation guide for individuals interested in various aspects of quantitative investing, including platforms, programming languages, mathematical foundations, machine learning, deep learning, and practical applications. The repository is well-structured and organized, with clear sections covering different topics. It includes resources on system platforms, programming codes, mathematical foundations, algorithm principles, machine learning, deep learning, reinforcement learning, graph networks, model deployment, and practical applications. Additionally, there are dedicated sections on quantitative trading and investment, as well as large models. The repository is actively maintained and updated, ensuring that users have access to the latest information and resources.

LLM-Navigation is a repository dedicated to documenting learning records related to large models, including basic knowledge, prompt engineering, building effective agents, model expansion capabilities, security measures against prompt injection, and applications in various fields such as AI agent control, browser automation, financial analysis, 3D modeling, and tool navigation using MCP servers. The repository aims to organize and collect information for personal learning and self-improvement through AI exploration.

FastDeploy is an inference and deployment toolkit for large language models and visual language models based on PaddlePaddle. It provides production-ready deployment solutions with core acceleration technologies such as load-balanced PD disaggregation, unified KV cache transmission, OpenAI API server compatibility, comprehensive quantization format support, advanced acceleration techniques, and multi-hardware support. The toolkit supports various hardware platforms like NVIDIA GPUs, Kunlunxin XPUs, Iluvatar GPUs, Enflame GCUs, and Hygon DCUs, with plans for expanding support to Ascend NPU and MetaX GPU. FastDeploy aims to optimize resource utilization, throughput, and performance for inference and deployment tasks.

This repository provides a comprehensive overview of large language models (LLMs), covering various aspects such as their history, types, underlying architecture, training techniques, and applications. It includes detailed explanations of key concepts like Transformer models, distributed training, fine-tuning, and reinforcement learning. The repository also discusses the evaluation and limitations of LLMs, including the phenomenon of hallucinations. Additionally, it provides a list of related courses and references for further exploration.

hongbomiao.com is a personal research and development (R&D) lab that facilitates the sharing of knowledge. The repository covers a wide range of topics including web development, mobile development, desktop applications, API servers, cloud native technologies, data processing, machine learning, computer vision, embedded systems, simulation, database management, data cleaning, data orchestration, testing, ops, authentication, authorization, security, system tools, reverse engineering, Ethereum, hardware, network, guidelines, design, bots, and more. It provides detailed information on various tools, frameworks, libraries, and platforms used in these domains.

DeepBattler is a tool designed for Hearthstone Battlegrounds players, providing real-time strategic advice and insights to improve gameplay experience. It integrates with the Hearthstone Deck Tracker plugin and offers voice-assisted guidance. The tool is powered by a large language model (LLM) and can match the strength of top players on EU servers. Users can set up the tool by adding dependencies, configuring the plugin path, and launching the LLM agent. DeepBattler is licensed for personal, educational, and non-commercial use, with guidelines on non-commercial distribution and acknowledgment of external contributions.

This repository is a collection of free large language models (LLMs) that can be used for various natural language processing tasks. It includes information on different free LLM APIs and projects that can be deployed without cost. Users can find details on the performance, login requirements, function calling capabilities, and deployment environments of each listed LLM source.

LogChat is an open-source and free AI chat client that supports various chat models and technologies such as ChatGPT, 讯飞星火, DeepSeek, LLM, TTS, STT, and Live2D. The tool provides a user-friendly interface designed using Qt Creator and can be used on Windows systems without any additional environment requirements. Users can interact with different AI models, perform voice synthesis and recognition, and customize Live2D character models. LogChat also offers features like language translation, AI platform integration, and menu items like screenshot editing, clock, and application launcher.

Exploring the integration between Nix and AI coding agents, this repository serves as a testbed for packaging, sandboxing, and enhancing AI-powered development tools within the Nix ecosystem. It provides a collection of AI tools with descriptions, versions, sources, licenses, homepages, and usage instructions. The repository also supports daily updates using GitHub Actions and offers a platform for experimental features like sandboxed execution, provider abstraction, and tool composition in Nix environments. Contributions are welcome, and the Nix packaging code in this repository is licensed under MIT.

Blueking Lite is an AI First lightweight operation product with low deployment resource requirements, low usage costs, and progressive experience, providing essential tools for operation administrators.

AI-Catalog is a curated list of AI tools, platforms, and resources across various domains. It serves as a comprehensive repository for users to discover and explore a wide range of AI applications. The catalog includes tools for tasks such as text-to-image generation, summarization, prompt generation, writing assistance, code assistance, developer tools, low code/no code tools, audio editing, video generation, 3D modeling, search engines, chatbots, email assistants, fun tools, gaming, music generation, presentation tools, website builders, education assistants, autonomous AI agents, photo editing, AI extensions, deep face/deep fake detection, text-to-speech, startup tools, SQL-related AI tools, education tools, and text-to-video conversion.

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 provides a comprehensive collection of AI tools and resources to enhance literature reviews. It includes a curated list of AI tools for various tasks, such as identifying research gaps, discovering relevant papers, visualizing paper content, and summarizing text. Additionally, the repository offers materials on generative AI, effective prompts, copywriting, image creation, and showcases of AI capabilities. By leveraging these tools and resources, researchers can streamline their literature review process, gain deeper insights from scholarly literature, and improve the quality of their research outputs.

Paper-ai is a tool that helps you write papers using artificial intelligence. It provides features such as AI writing assistance, reference searching, and editing and formatting tools. With Paper-ai, you can quickly and easily create high-quality papers.

PaperQA is a minimal package for question and answering from PDFs or text files, providing very good answers with in-text citations. It uses OpenAI Embeddings to embed and search documents, and follows a process of embedding docs and queries, searching for top passages, creating summaries, scoring and selecting relevant summaries, putting summaries into prompt, and generating answers. Users can customize prompts and use various models for embeddings and LLMs. The tool can be used asynchronously and supports adding documents from paths, files, or URLs.

ChatData is a robust chat-with-documents application designed to extract information and provide answers by querying the MyScale free knowledge base or uploaded documents. It leverages the Retrieval Augmented Generation (RAG) framework, millions of Wikipedia pages, and arXiv papers. Features include self-querying retriever, VectorSQL, session management, and building a personalized knowledge base. Users can effortlessly navigate vast data, explore academic papers, and research documents. ChatData empowers researchers, students, and knowledge enthusiasts to unlock the true potential of information retrieval.

noScribe is an AI-based software designed for automated audio transcription, specifically tailored for transcribing interviews for qualitative social research or journalistic purposes. It is a free and open-source tool that runs locally on the user's computer, ensuring data privacy. The software can differentiate between speakers and supports transcription in 99 languages. It includes a user-friendly editor for reviewing and correcting transcripts. Developed by Kai Dröge, a PhD in sociology with a background in computer science, noScribe aims to streamline the transcription process and enhance the efficiency of qualitative analysis.

AI Study Assistant is an app designed to enhance learning experience and boost academic performance. It serves as a personal tutor, lecture summarizer, writer, and question generator powered by Google PaLM 2. Features include interacting with an AI chatbot, summarizing lectures, generating essays, and creating practice questions. The app is built using 100% Kotlin, Jetpack Compose, Clean Architecture, and MVVM design pattern, with technologies like Ktor, Room DB, Hilt, and Kotlin coroutines. AI Study Assistant aims to provide comprehensive AI-powered assistance for students in various academic tasks.

Data-to-paper is an AI-driven framework designed to guide users through the process of conducting end-to-end scientific research, starting from raw data to the creation of comprehensive and human-verifiable research papers. The framework leverages a combination of LLM and rule-based agents to assist in tasks such as hypothesis generation, literature search, data analysis, result interpretation, and paper writing. It aims to accelerate research while maintaining key scientific values like transparency, traceability, and verifiability. The framework is field-agnostic, supports both open-goal and fixed-goal research, creates data-chained manuscripts, involves human-in-the-loop interaction, and allows for transparent replay of the research process.

K2 (GeoLLaMA) is a large language model for geoscience, trained on geoscience literature and fine-tuned with knowledge-intensive instruction data. It outperforms baseline models on objective and subjective tasks. The repository provides K2 weights, core data of GeoSignal, GeoBench benchmark, and code for further pretraining and instruction tuning. The model is available on Hugging Face for use. The project aims to create larger and more powerful geoscience language models in the future.