A repository providing basic concepts, technical challenges, and resources on data engineering in Spanish. It is a curated list of free, Spanish-language materials found on the internet to facilitate the study of data engineering enthusiasts. The repository covers programming fundamentals, programming languages like Python, version control with Git, database fundamentals, SQL, design concepts, Big Data, analytics, cloud computing, data processing, and job search tips in the IT field.

Ai Hoshino - MD is a WhatsApp bot tool with features like voice and text interaction, group configuration, anti-delete, anti-link, personalized welcome messages, chatbot functionality, sticker creation, sub-bot integration, RPG game, YouTube music and video downloads, and more. The tool is actively maintained by Starlights Team and offers a range of functionalities for WhatsApp users.

LLMs-Zero-to-Hero is a repository dedicated to training large language models (LLMs) from scratch, covering topics such as dense models, MOE models, pre-training, supervised fine-tuning, direct preference optimization, reinforcement learning from human feedback, and deploying large models. The repository provides detailed learning notes for different chapters, code implementations, and resources for training and deploying LLMs. It aims to guide users from being beginners to proficient in building and deploying large language models.

AI-LLM-ML-CS-Quant-Overview is a repository providing overview notes on AI, Large Language Models (LLM), Machine Learning (ML), Computer Science (CS), and Quantitative Finance. It covers various topics such as LangGraph & Cursor AI, DeepSeek, MoE (Mixture of Experts), NVIDIA GTC, LLM Essentials, System Design, Computer Systems, Big Data and AI in Finance, Econometrics and Statistics Conference, C++ Design Patterns and Derivatives Pricing, High-Frequency Finance, Machine Learning for Algorithmic Trading, Stochastic Volatility Modeling, Quant Job Interview Questions, Distributed Systems, Language Models, Designing Machine Learning Systems, Designing Data-Intensive Applications (DDIA), Distributed Machine Learning, and The Elements of Quantitative Investing.

Plug-play-modules is a comprehensive collection of plug-and-play modules for AI, deep learning, and computer vision applications. It includes various convolution variants, latest attention mechanisms, feature fusion modules, up-sampling/down-sampling modules, suitable for tasks like image classification, object detection, instance segmentation, semantic segmentation, single object tracking (SOT), multi-object tracking (MOT), infrared object tracking (RGBT), image de-raining, de-fogging, de-blurring, super-resolution, and more. The modules are designed to enhance model performance and feature extraction capabilities across various tasks.

Awesome-Graph-LLM is a curated collection of research papers exploring the intersection of graph-based techniques with Large Language Models (LLMs). The repository aims to bridge the gap between LLMs and graph structures prevalent in real-world applications by providing a comprehensive list of papers covering various aspects of graph reasoning, node classification, graph classification/regression, knowledge graphs, multimodal models, applications, and tools. It serves as a valuable resource for researchers and practitioners interested in leveraging LLMs for graph-related tasks.

This repository provides an in-depth review of industry trends in AI, Large Language Models (LLMs), Machine Learning, Computer Science, and Quantitative Finance. It covers various topics such as NVIDIA GTC conferences, DeepSeek theory and applications, LangGraph & Cursor AI, LLM essentials, system design, computer systems, big data and AI in finance, C++ design patterns, high-frequency finance, machine learning for algorithmic trading, stochastic volatility modeling, and quant job interview questions.

AI-LLM-ML-CS-Quant-Readings is a repository dedicated to taking notes on Artificial Intelligence, Large Language Models, Machine Learning, Computer Science, and Quantitative Finance. It contains a wide range of resources, including theory, applications, conferences, essentials, foundations, system design, computer systems, finance, and job interview questions. The repository covers topics such as AI systems, multi-agent systems, deep learning theory and applications, system design interviews, C++ design patterns, high-frequency finance, algorithmic trading, stochastic volatility modeling, and quantitative investing. It is a comprehensive collection of materials for individuals interested in these fields.

ERNIE SDK repository contains two projects: ERNIE Bot Agent and ERNIE Bot. ERNIE Bot Agent is a large model intelligent agent development framework based on the Wenxin large model orchestration capability introduced by Baidu PaddlePaddle, combined with the rich preset platform functions of the PaddlePaddle Star River community. ERNIE Bot provides developers with convenient interfaces to easily call the Wenxin large model for text creation, general conversation, semantic vectors, and AI drawing basic functions.

The Qianfan SDK provides best practices for large model toolchains, allowing AI workflows and AI-native applications to access the Qianfan large model platform elegantly and conveniently. The core capabilities of the SDK include three parts: large model reasoning, large model training, and general and extension: * `Large model reasoning`: Implements interface encapsulation for reasoning of Yuyan (ERNIE-Bot) series, open source large models, etc., supporting dialogue, completion, Embedding, etc. * `Large model training`: Based on platform capabilities, it supports end-to-end large model training process, including training data, fine-tuning/pre-training, and model services. * `General and extension`: General capabilities include common AI development tools such as Prompt/Debug/Client. The extension capability is based on the characteristics of Qianfan to adapt to common middleware frameworks.

Nekro Agent is an AI chat plugin and proxy execution bot that is highly scalable, offers high freedom, and has minimal deployment requirements. It features context-aware chat for group/private chats, custom character settings, sandboxed execution environment, interactive image resource handling, customizable extension development interface, easy deployment with docker-compose, integration with Stable Diffusion for AI drawing capabilities, support for various file types interaction, hot configuration updates and command control, native multimodal understanding, visual application management control panel, CoT (Chain of Thought) support, self-triggered timers and holiday greetings, event notification understanding, and more. It allows for third-party extensions and AI-generated extensions, and includes features like automatic context trigger based on LLM, and a variety of basic commands for bot administrators.

The ai-chat-local repository is a project that implements a local model customization using ollama, allowing data privacy and bypassing detection. It integrates with the esp32 development board to create a simple chat assistant. The project includes features like database integration for chat persistence, user recognition, and AI-based document processing. It also offers a graphical configuration tool for Dify.AI implementation.

jvm-openai is a minimalistic unofficial OpenAI API client for the JVM, written in Java. It serves as a Java client for OpenAI API with a focus on simplicity and minimal dependencies. The tool provides support for various OpenAI APIs and endpoints, including Audio, Chat, Embeddings, Fine-tuning, Batch, Files, Uploads, Images, Models, Moderations, Assistants, Threads, Messages, Runs, Run Steps, Vector Stores, Vector Store Files, Vector Store File Batches, Invites, Users, Projects, Project Users, Project Service Accounts, Project API Keys, and Audit Logs. Users can easily integrate this tool into their Java projects to interact with OpenAI services efficiently.

LLaMA Factory is a unified framework for fine-tuning 100+ large language models (LLMs) with various methods, including pre-training, supervised fine-tuning, reward modeling, PPO, DPO and ORPO. It features integrated algorithms like GaLore, BAdam, DoRA, LongLoRA, LLaMA Pro, LoRA+, LoftQ and Agent tuning, as well as practical tricks like FlashAttention-2, Unsloth, RoPE scaling, NEFTune and rsLoRA. LLaMA Factory provides experiment monitors like LlamaBoard, TensorBoard, Wandb, MLflow, etc., and supports faster inference with OpenAI-style API, Gradio UI and CLI with vLLM worker. Compared to ChatGLM's P-Tuning, LLaMA Factory's LoRA tuning offers up to 3.7 times faster training speed with a better Rouge score on the advertising text generation task. By leveraging 4-bit quantization technique, LLaMA Factory's QLoRA further improves the efficiency regarding the GPU memory.

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.

AirGo is a front and rear end separation, multi user, multi protocol proxy service management system, simple and easy to use. It supports vless, vmess, shadowsocks, and hysteria2.

Mosec is a high-performance and flexible model serving framework for building ML model-enabled backend and microservices. It bridges the gap between any machine learning models you just trained and the efficient online service API. * **Highly performant** : web layer and task coordination built with Rust 🦀, which offers blazing speed in addition to efficient CPU utilization powered by async I/O * **Ease of use** : user interface purely in Python 🐍, by which users can serve their models in an ML framework-agnostic manner using the same code as they do for offline testing * **Dynamic batching** : aggregate requests from different users for batched inference and distribute results back * **Pipelined stages** : spawn multiple processes for pipelined stages to handle CPU/GPU/IO mixed workloads * **Cloud friendly** : designed to run in the cloud, with the model warmup, graceful shutdown, and Prometheus monitoring metrics, easily managed by Kubernetes or any container orchestration systems * **Do one thing well** : focus on the online serving part, users can pay attention to the model optimization and business logic

The 'llm-code-interpreter' repository is a deprecated plugin that provides a code interpreter on steroids for ChatGPT by E2B. It gives ChatGPT access to a sandboxed cloud environment with capabilities like running any code, accessing Linux OS, installing programs, using filesystem, running processes, and accessing the internet. The plugin exposes commands to run shell commands, read files, and write files, enabling various possibilities such as running different languages, installing programs, starting servers, deploying websites, and more. It is powered by the E2B API and is designed for agents to freely experiment within a sandboxed environment.

Pezzo is a fully cloud-native and open-source LLMOps platform that allows users to observe and monitor AI operations, troubleshoot issues, save costs and latency, collaborate, manage prompts, and deliver AI changes instantly. It supports various clients for prompt management, observability, and caching. Users can run the full Pezzo stack locally using Docker Compose, with prerequisites including Node.js 18+, Docker, and a GraphQL Language Feature Support VSCode Extension. Contributions are welcome, and the source code is available under the Apache 2.0 License.

Learn Cloud Applied Generative AI Engineering (GenEng) is a course focusing on the application of generative AI technologies in various industries. The course covers topics such as the economic impact of generative AI, the role of developers in adopting and integrating generative AI technologies, and the future trends in generative AI. Students will learn about tools like OpenAI API, LangChain, and Pinecone, and how to build and deploy Large Language Models (LLMs) for different applications. The course also explores the convergence of generative AI with Web 3.0 and its potential implications for decentralized intelligence.

This repository contains shared codebase for two projects: gcloud-aio and gcloud-rest. gcloud-aio is built for Python 3's asyncio, while gcloud-rest is a threadsafe requests-based implementation. It provides clients for Google Cloud services like Auth, BigQuery, Datastore, KMS, PubSub, Storage, and Task Queue. Users can install the library using pip and refer to the documentation for usage details. Developers can contribute to the project by following the contribution guide.

Fluid is an open source Kubernetes-native Distributed Dataset Orchestrator and Accelerator for data-intensive applications, such as big data and AI applications. It implements dataset abstraction, scalable cache runtime, automated data operations, elasticity and scheduling, and is runtime platform agnostic. Key concepts include Dataset and Runtime. Prerequisites include Kubernetes version > 1.16, Golang 1.18+, and Helm 3. The tool offers features like accelerating remote file accessing, machine learning, accelerating PVC, preloading dataset, and on-the-fly dataset cache scaling. Contributions are welcomed, and the project is under the Apache 2.0 license with a vendor-neutral approach.

AIGES is a core component of the Athena Serving Framework, designed as a universal encapsulation tool for AI developers to deploy AI algorithm models and engines quickly. By integrating AIGES, you can deploy AI algorithm models and engines rapidly and host them on the Athena Serving Framework, utilizing supporting auxiliary systems for networking, distribution strategies, data processing, etc. The Athena Serving Framework aims to accelerate the cloud service of AI algorithm models and engines, providing multiple guarantees for cloud service stability through cloud-native architecture. You can efficiently and securely deploy, upgrade, scale, operate, and monitor models and engines without focusing on underlying infrastructure and service-related development, governance, and operations.