Awesome AI Efficiency is a curated list of resources dedicated to enhancing efficiency in AI systems. The repository covers various topics essential for optimizing AI models and processes, aiming to make AI faster, cheaper, smaller, and greener. It includes topics like quantization, pruning, caching, distillation, factorization, compilation, parameter-efficient fine-tuning, speculative decoding, hardware optimization, training techniques, inference optimization, sustainability strategies, and scalability approaches.

Open-dLLM is the most open release of a diffusion-based large language model, providing pretraining, evaluation, inference, and checkpoints. It introduces Open-dCoder, the code-generation variant of Open-dLLM. The repo offers a complete stack for diffusion LLMs, enabling users to go from raw data to training, checkpoints, evaluation, and inference in one place. It includes pretraining pipeline with open datasets, inference scripts for easy sampling and generation, evaluation suite with various metrics, weights and checkpoints on Hugging Face, and transparent configs for full reproducibility.

The 'END TO END GENERATIVE AI PROJECTS' repository is a collection of awesome industry projects utilizing Large Language Models (LLM) for various tasks such as chat applications with PDFs, image to speech generation, video transcribing and summarizing, resume tracking, text to SQL conversion, invoice extraction, medical chatbot, financial stock analysis, and more. The projects showcase the deployment of LLM models like Google Gemini Pro, HuggingFace Models, OpenAI GPT, and technologies such as Langchain, Streamlit, LLaMA2, LLaMAindex, and more. The repository aims to provide end-to-end solutions for different AI applications.

FaceAI SDK is an on-device offline face detection, recognition, liveness detection, anti-spoofing, and 1:N/M:N face search SDK. It enables quick integration to achieve on-device face recognition, face search, and other functions. The SDK performs all functions offline on the device without the need for internet connection, ensuring privacy and security. It supports various actions for liveness detection, custom camera management, and clear imaging even in challenging lighting conditions.

Awesome LangGraph is a curated list of projects, resources, and tools for building stateful, multi-actor applications with LangGraph. It provides valuable resources for developers at all stages of development, from beginners to those building production-ready systems. The repository covers core ecosystem components, LangChain ecosystem, LangGraph platform, official resources, starter templates, pre-built agents, example applications, development tools, community projects, AI assistants, content & media, knowledge & retrieval, finance & business, sustainability, learning resources, companies using LangGraph, contributing guidelines, and acknowledgments.

HaE is a framework project in the field of network security (data security) that combines artificial intelligence (AI) large models to achieve highlighting and information extraction of HTTP messages (including WebSocket). It aims to reduce testing time, focus on valuable and meaningful messages, and improve vulnerability discovery efficiency. The project provides a clear and visual interface design, simple interface interaction, and centralized data panel for querying and extracting information. It also features built-in color upgrade algorithm, one-click export/import of data, and integration of AI large models API for optimized data processing.

Nocode/WEP is a forward-looking office visualization platform that includes modules for document building, web application creation, presentation design, and AI capabilities for office scenarios. It supports features such as configuring bullet comments, global article comments, multimedia content, custom drawing boards, flowchart editor, form designer, keyword annotations, article statistics, custom appreciation settings, JSON import/export, content block copying, and unlimited hierarchical directories. The platform is compatible with major browsers and aims to deliver content value, iterate products, share technology, and promote open-source collaboration.

Happy-LLM is a systematic learning tutorial for Large Language Models (LLM) that covers NLP research methods, LLM architecture, training process, and practical applications. It aims to help readers understand the principles and training processes of large language models. The tutorial delves into Transformer architecture, attention mechanisms, pre-training language models, building LLMs, training processes, and practical applications like RAG and Agent technologies. It is suitable for students, researchers, and LLM enthusiasts with programming experience, Python knowledge, and familiarity with deep learning and NLP concepts. The tutorial encourages hands-on practice and participation in LLM projects and competitions to deepen understanding and contribute to the open-source LLM community.

Awesome-LLM-RAG-Application is a repository that provides resources and information about applications based on Large Language Models (LLM) with Retrieval-Augmented Generation (RAG) pattern. It includes a survey paper, GitHub repo, and guides on advanced RAG techniques. The repository covers various aspects of RAG, including academic papers, evaluation benchmarks, downstream tasks, tools, and technologies. It also explores different frameworks, preprocessing tools, routing mechanisms, evaluation frameworks, embeddings, security guardrails, prompting tools, SQL enhancements, LLM deployment, observability tools, and more. The repository aims to offer comprehensive knowledge on RAG for readers interested in exploring and implementing LLM-based systems and products.

LlamaV-o1 is a Large Multimodal Model designed for spontaneous reasoning tasks. It outperforms various existing models on multimodal reasoning benchmarks. The project includes a Step-by-Step Visual Reasoning Benchmark, a novel evaluation metric, and a combined Multi-Step Curriculum Learning and Beam Search Approach. The model achieves superior performance in complex multi-step visual reasoning tasks in terms of accuracy and efficiency.

Step-DPO is a method for enhancing long-chain reasoning ability of LLMs with a data construction pipeline creating a high-quality dataset. It significantly improves performance on math and GSM8K tasks with minimal data and training steps. The tool fine-tunes pre-trained models like Qwen2-7B-Instruct with Step-DPO, achieving superior results compared to other models. It provides scripts for training, evaluation, and deployment, along with examples and acknowledgements.

VLM Run Cookbook is a repository containing practical examples and tutorials for extracting structured data from images, videos, and documents using Vision Language Models (VLMs). It offers comprehensive Colab notebooks demonstrating real-world applications of VLM Run, with complete code and documentation for easy adaptation. The examples cover various domains such as financial documents and TV news analysis.

AI 0x0 is a versatile AI query generation desktop floating assistant application that supports MacOS and Windows. It allows users to utilize AI capabilities in any desktop software to query and generate text, images, audio, and video data, helping them work more efficiently. The application features a dynamic desktop floating ball, floating dialogue bubbles, customizable presets, conversation bookmarking, preset packages, network acceleration, query mode, input mode, mouse navigation, deep customization of ChatGPT Next Web, support for full-format libraries, online search, voice broadcasting, voice recognition, voice assistant, application plugins, multi-model support, online text and image generation, image recognition, frosted glass interface, light and dark theme adaptation for each language model, and free access to all language models except Chat0x0 with a key.

nndeploy is a tool that allows you to quickly build your visual AI workflow without the need for frontend technology. It provides ready-to-use algorithm nodes for non-AI programmers, including large language models, Stable Diffusion, object detection, image segmentation, etc. The workflow can be exported as a JSON configuration file, supporting Python/C++ API for direct loading and running, deployment on cloud servers, desktops, mobile devices, edge devices, and more. The framework includes mainstream high-performance inference engines and deep optimization strategies to help you transform your workflow into enterprise-level production applications.

The `ipex-llm` repository is an LLM acceleration library designed for Intel GPU, NPU, and CPU. It provides seamless integration with various models and tools like llama.cpp, Ollama, HuggingFace transformers, LangChain, LlamaIndex, vLLM, Text-Generation-WebUI, DeepSpeed-AutoTP, FastChat, Axolotl, and more. The library offers optimizations for over 70 models, XPU acceleration, and support for low-bit (FP8/FP6/FP4/INT4) operations. Users can run different models on Intel GPUs, NPU, and CPUs with support for various features like finetuning, inference, serving, and benchmarking.

Awesome AI Efficiency is a curated list of resources dedicated to enhancing efficiency in AI systems. The repository covers various topics essential for optimizing AI models and processes, aiming to make AI faster, cheaper, smaller, and greener. It includes topics like quantization, pruning, caching, distillation, factorization, compilation, parameter-efficient fine-tuning, speculative decoding, hardware optimization, training techniques, inference optimization, sustainability strategies, and scalability approaches.

A curated list of high-quality papers on resource-efficient Large Language Models (LLMs) with a focus on various aspects such as architecture design, pre-training, fine-tuning, inference, system design, and evaluation metrics. The repository covers topics like efficient transformer architectures, non-transformer architectures, memory efficiency, data efficiency, model compression, dynamic acceleration, deployment optimization, support infrastructure, and other related systems. It also provides detailed information on computation metrics, memory metrics, energy metrics, financial cost metrics, network communication metrics, and other metrics relevant to resource-efficient LLMs. The repository includes benchmarks for evaluating the efficiency of NLP models and references for further reading.

AIMET is a library that provides advanced model quantization and compression techniques for trained neural network models. It provides features that have been proven to improve run-time performance of deep learning neural network models with lower compute and memory requirements and minimal impact to task accuracy. AIMET is designed to work with PyTorch, TensorFlow and ONNX models. We also host the AIMET Model Zoo - a collection of popular neural network models optimized for 8-bit inference. We also provide recipes for users to quantize floating point models using AIMET.

Intel® Neural Compressor is an open-source Python library that supports popular model compression techniques such as quantization, pruning (sparsity), distillation, and neural architecture search on mainstream frameworks such as TensorFlow, PyTorch, ONNX Runtime, and MXNet. It provides key features, typical examples, and open collaborations, including support for a wide range of Intel hardware, validation of popular LLMs, and collaboration with cloud marketplaces, software platforms, and open AI ecosystems.

This repository is dedicated to the pruning of large language models (LLMs). It aims to serve as a comprehensive resource for researchers and practitioners interested in the efficient reduction of model size while maintaining or enhancing performance. The repository contains various papers, summaries, and links related to different pruning approaches for LLMs, along with author information and publication details. It covers a wide range of topics such as structured pruning, unstructured pruning, semi-structured pruning, and benchmarking methods. Researchers and practitioners can explore different pruning techniques, understand their implications, and access relevant resources for further study and implementation.

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.