The 'ai-by-hand-excel' repository is a collection of AI exercises that can be implemented manually using Excel. It includes both basic and advanced topics such as Softmax, LeakyReLU, Backpropagation, Transformer, RNN, and Mamba. The repository aims to provide hands-on experience and understanding of AI concepts through practical Excel exercises.

AI Engineering Academy aims to provide a structured learning path for individuals looking to learn Applied AI effectively. The platform offers multiple roadmaps covering topics like Retrieval Augmented Generation, Fine-tuning, and Deployment. Each roadmap equips learners with the knowledge and skills needed to excel in applied GenAI. Additionally, the platform will feature Hands-on End-to-End AI projects in the future.

This repository provides a comprehensive guide to training, evaluating, and building applications with Large Language Models (LLMs). It covers various aspects of LLMs, including pretraining, fine-tuning, reinforcement learning from human feedback (RLHF), and more. The repository also includes practical examples and code snippets to help users get started with LLMs quickly and easily.

Genesis is a physics platform designed for general purpose Robotics/Embodied AI/Physical AI applications. It includes a universal physics engine, a lightweight, ultra-fast, pythonic, and user-friendly robotics simulation platform, a powerful and fast photo-realistic rendering system, and a generative data engine that transforms user-prompted natural language description into various modalities of data. It aims to lower the barrier to using physics simulations, unify state-of-the-art physics solvers, and minimize human effort in collecting and generating data for robotics and other domains.

SLAM-LLM is a deep learning toolkit designed for researchers and developers to train custom multimodal large language models (MLLM) focusing on speech, language, audio, and music processing. It provides detailed recipes for training and high-performance checkpoints for inference. The toolkit supports tasks such as automatic speech recognition (ASR), text-to-speech (TTS), visual speech recognition (VSR), automated audio captioning (AAC), spatial audio understanding, and music caption (MC). SLAM-LLM features easy extension to new models and tasks, mixed precision training for faster training with less GPU memory, multi-GPU training with data and model parallelism, and flexible configuration based on Hydra and dataclass.

This tutorial provides a comprehensive guide on building a small Large Language Model (LLM) from scratch using PyTorch. The author shares insights and experiences gained from working on LLM projects in the industry, aiming to help beginners understand the fundamental components of LLMs and training fine-tuning codes. The tutorial covers topics such as model structure overview, attention modules, optimization techniques, normalization layers, tokenizers, pretraining, and fine-tuning with dialogue data. It also addresses specific industry-related challenges and explores cutting-edge model concepts like DeepSeek network structure, causal attention, dynamic-to-static tensor conversion for ONNX inference, and performance optimizations for NPU chips. The series emphasizes hands-on practice with small models to enable local execution and plans to expand into multimodal language models and tensor parallel multi-card deployment.

SLAM-LLM is a deep learning toolkit for training custom multimodal large language models (MLLM) focusing on speech, language, audio, and music processing. It provides detailed recipes for training and high-performance checkpoints for inference. The toolkit supports various tasks such as automatic speech recognition (ASR), text-to-speech (TTS), visual speech recognition (VSR), automated audio captioning (AAC), spatial audio understanding, and music caption (MC). Users can easily extend to new models and tasks, utilize mixed precision training for faster training with less GPU memory, and perform multi-GPU training with data and model parallelism. Configuration is flexible based on Hydra and dataclass, allowing different configuration methods.

FuseAI is a repository that focuses on knowledge fusion of large language models. It includes FuseChat, a state-of-the-art 7B LLM on MT-Bench, and FuseLLM, which surpasses Llama-2-7B by fusing three open-source foundation LLMs. The repository provides tech reports, releases, and datasets for FuseChat and FuseLLM, showcasing their performance and advancements in the field of chat models and large language models.

JoliGEN is an integrated framework for training custom generative AI image-to-image models. It implements GAN, Diffusion, and Consistency models for various image translation tasks, including domain and style adaptation with conservation of semantics. The tool is designed for real-world applications such as Controlled Image Generation, Augmented Reality, Dataset Smart Augmentation, and Synthetic to Real transforms. JoliGEN allows for fast and stable training with a REST API server for simplified deployment. It offers a wide range of options and parameters with detailed documentation available for models, dataset formats, and data augmentation.

Cleanlab helps you **clean** data and **lab** els by automatically detecting issues in a ML dataset. To facilitate **machine learning with messy, real-world data** , this data-centric AI package uses your _existing_ models to estimate dataset problems that can be fixed to train even _better_ models.

This repository is a collection of free resources and a roadmap designed to help individuals learn Machine Learning and Artificial Intelligence concepts by providing key concepts, building blocks, roles, a learning roadmap, courses, certifications, books, tools & frameworks, research blogs, applied blogs, practice problems, communities, YouTube channels, newsletters, and must-read papers. It covers a wide range of topics from supervised learning to MLOps, offering guidance on learning paths, practical experience, and job interview preparation.

Only Train Once (OTO) is an automatic, architecture-agnostic DNN training and compression framework that allows users to train a general DNN from scratch or a pretrained checkpoint to achieve high performance and slimmer architecture simultaneously in a one-shot manner without fine-tuning. The framework includes features for automatic structured pruning and erasing operators, as well as hybrid structured sparse optimizers for efficient model compression. OTO provides tools for pruning zero-invariant group partitioning, constructing pruned models, and visualizing pruning and erasing dependency graphs. It supports the HESSO optimizer and offers a sanity check for compliance testing on various DNNs. The repository also includes publications, installation instructions, quick start guides, and a roadmap for future enhancements and collaborations.

AgentCPM is a series of open-source LLM agents jointly developed by THUNLP, Renmin University of China, ModelBest, and the OpenBMB community. It addresses challenges faced by agents in real-world applications such as limited long-horizon capability, autonomy, and generalization. The team focuses on building deep research capabilities for agents, releasing AgentCPM-Explore, a deep-search LLM agent, and AgentCPM-Report, a deep-research LLM agent. AgentCPM-Explore is the first open-source agent model with 4B parameters to appear on widely used long-horizon agent benchmarks. AgentCPM-Report is built on the 8B-parameter base model MiniCPM4.1, autonomously generating long-form reports with extreme performance and minimal footprint, designed for high-privacy scenarios with offline and agile local deployment.

A repository sharing the panorama of the methodology literature on Transformer architecture upgrades in Large Language Models for handling extensive context windows, with real-time updating the newest published works. It includes a survey on advancing Transformer architecture in long-context large language models, flash-ReRoPE implementation, latest news on data engineering, lightning attention, Kimi AI assistant, chatglm-6b-128k, gpt-4-turbo-preview, benchmarks like InfiniteBench and LongBench, long-LLMs-evals for evaluating methods for enhancing long-context capabilities, and LLMs-learning for learning technologies and applicated tasks about Large Language Models.

LLM is a repository focused on providing educational courses on artificial intelligence models, specifically focusing on language and image understanding. The courses are taught by Alireza Akhavan Pour and cover topics such as generative AI models and language-image understanding. The repository also includes information on how to contact the team through Telegram channels for course updates and Q&A sessions. Participants are encouraged to add their LLM or VLM course certificates to their LinkedIn profiles and tag Alireza Akhavan Pour for recognition and resume enhancement.

OpenNARS-for-Applications is an implementation of a Non-Axiomatic Reasoning System, a general-purpose reasoner that adapts under the Assumption of Insufficient Knowledge and Resources. The system combines the logic and conceptual ideas of OpenNARS, event handling and procedure learning capabilities of ANSNA and 20NAR1, and the control model from ALANN. It is written in C, offers improved reasoning performance, and has been compared with Reinforcement Learning and means-end reasoning approaches. The system has been used in real-world applications such as assisting first responders, real-time traffic surveillance, and experiments with autonomous robots. It has been developed with a pragmatic mindset focusing on effective implementation of existing theory.

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.

Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.

LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.

The VisionCraft API is a free API for using over 100 different AI models. From images to sound.

Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.

PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.

Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.

SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.

Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.