FlagPerf

FlagPerf is an integrated AI hardware evaluation engine jointly built by the Institute of Intelligence and AI hardware manufacturers. It aims to establish an industry-oriented metric system to evaluate the actual capabilities of AI hardware under software stack combinations (model + framework + compiler). FlagPerf features a multidimensional evaluation metric system that goes beyond just measuring 'whether the chip can support specific model training.' It covers various scenarios and tasks, including computer vision, natural language processing, speech, multimodal, with support for multiple training frameworks and inference engines to connect AI hardware with software ecosystems. It also supports various testing environments to comprehensively assess the performance of domestic AI chips in different scenarios.

Widgets is a desktop component front-end open source component. The project is still being continuously improved. The desktop component client can be downloaded and run in two ways: 1. https://www.microsoft.com/store/productId/9NPR50GQ7T53 2. https://widgetjs.cn After cloning the code, you need to download the dependency in the project directory: `shell pnpm install` and run: `shell pnpm serve`

Agentica is a human-centric framework for building large language model agents. It provides functionalities for planning, memory management, tool usage, and supports features like reflection, planning and execution, RAG, multi-agent, multi-role, and workflow. The tool allows users to quickly code and orchestrate agents, customize prompts, and make API calls to various services. It supports API calls to OpenAI, Azure, Deepseek, Moonshot, Claude, Ollama, and Together. Agentica aims to simplify the process of building AI agents by providing a user-friendly interface and a range of functionalities for agent development.

Llama中文社区是一个专注于Llama模型在中文方面的优化和上层建设的高级技术社区。 **已经基于大规模中文数据，从预训练开始对Llama2模型进行中文能力的持续迭代升级【Done】**。**正在对Llama3模型进行中文能力的持续迭代升级【Doing】** 我们热忱欢迎对大模型LLM充满热情的开发者和研究者加入我们的行列。

Video-subtitle-remover (VSR) is a software based on AI technology that removes hard subtitles from videos. It achieves the following functions: - Lossless resolution: Remove hard subtitles from videos, generate files with subtitles removed - Fill the region of removed subtitles using a powerful AI algorithm model (non-adjacent pixel filling and mosaic removal) - Support custom subtitle positions, only remove subtitles in defined positions (input position) - Support automatic removal of all text in the entire video (no input position required) - Support batch removal of watermark text from multiple images.

This repository aims to build a low-parameter LLM model through pretraining, fine-tuning, model rewarding, and reinforcement learning stages to create a chat model capable of simple conversation tasks. It features using the bert4torch training framework, seamless integration with transformers package for inference, optimized file reading during training to reduce memory usage, providing complete training logs for reproducibility, and the ability to customize robot attributes. The chat model supports multi-turn conversations. The trained model currently only supports basic chat functionality due to limitations in corpus size, model scale, SFT corpus size, and quality.

Cube Studio is an open-source all-in-one cloud-native machine learning platform that provides various functionalities such as project group management, network configuration, user management, role management, billing functions, SSO single sign-on, support for multiple computing power types, support for multiple resource groups and clusters, edge cluster support, serverless cluster mode support, database storage support, machine resource management, storage disk management, internationalization capabilities, data map management, data calculation, ETL orchestration, data set management, data annotation, image/audio/text dataset support, feature processing, traditional machine learning algorithms, distributed deep learning frameworks, distributed acceleration frameworks, model evaluation, model format conversion, model registration, model deployment, distributed media processing, custom operators, automatic learning, custom training images, automatic parameter tuning, TensorBoard jobs, internal services, model management, inference services, monitoring, model application management, model marketplace, model development, model fine-tuning, web model deployment, automated annotation, dataset SDK, notebook SDK, pipeline training SDK, inference service SDK, large model distributed training, large model inference, large model fine-tuning, intelligent conversation, private knowledge base, model deployment for WeChat public accounts, enterprise WeChat group chatbot integration, DingTalk group chatbot integration, and more. Cube Studio offers template-based functionality for data import/export, data processing, feature processing, machine learning frameworks, machine learning algorithms, deep learning frameworks, model processing, model serving, monitoring, and more.

Chatluna is a machine learning model plugin that provides chat services with large language models. It is highly extensible, supports multiple output formats, and offers features like custom conversation presets, rate limiting, and context awareness. Users can deploy Chatluna under Koishi without additional configuration. The plugin supports various models/platforms like OpenAI, Azure OpenAI, Google Gemini, and more. It also provides preset customization using YAML files and allows for easy forking and development within Koishi projects. However, the project lacks web UI, HTTP server, and project documentation, inviting contributions from the community.

WeST is a Speech Recognition/Transcript tool developed in 300 lines of code, inspired by SLAM-ASR and LLaMA 3.1. The model includes a Language Model (LLM), a Speech Encoder, and a trainable Projector. It requires training data in jsonl format with 'wav' and 'txt' entries. WeST can be used for training and decoding speech recognition models.

Peter Cat is an intelligent Q&A chatbot solution designed for community maintainers and developers. It provides a conversational Q&A agent configuration system, self-hosting deployment solutions, and a convenient integrated application SDK. Users can easily create intelligent Q&A chatbots for their GitHub repositories and quickly integrate them into various official websites or projects to provide more efficient technical support for the community.

Chinese-Mixtral-8x7B is an open-source project based on Mistral's Mixtral-8x7B model for incremental pre-training of Chinese vocabulary, aiming to advance research on MoE models in the Chinese natural language processing community. The expanded vocabulary significantly improves the model's encoding and decoding efficiency for Chinese, and the model is pre-trained incrementally on a large-scale open-source corpus, enabling it with powerful Chinese generation and comprehension capabilities. The project includes a large model with expanded Chinese vocabulary and incremental pre-training code.

FlagPerf is an integrated AI hardware evaluation engine jointly built by the Institute of Intelligence and AI hardware manufacturers. It aims to establish an industry-oriented metric system to evaluate the actual capabilities of AI hardware under software stack combinations (model + framework + compiler). FlagPerf features a multidimensional evaluation metric system that goes beyond just measuring 'whether the chip can support specific model training.' It covers various scenarios and tasks, including computer vision, natural language processing, speech, multimodal, with support for multiple training frameworks and inference engines to connect AI hardware with software ecosystems. It also supports various testing environments to comprehensively assess the performance of domestic AI chips in different scenarios.

TurnkeyML is a tools framework that integrates models, toolchains, and hardware backends to simplify the evaluation and actuation of deep learning models. It supports use cases like exporting ONNX files, performance validation, functional coverage measurement, stress testing, and model insights analysis. The framework consists of analysis, build, runtime, reporting tools, and a models corpus, seamlessly integrated to provide comprehensive functionality with simple commands. Extensible through plugins, it offers support for various export and optimization tools and AI runtimes. The project is actively seeking collaborators and is licensed under Apache 2.0.

This repository contains the source code for the userspace level runtime driver for Coral devices. The software is distributed in binary form at coral.ai/software. Users can build the library using Docker + Bazel, Bazel, or Makefile methods. It supports building on Linux, macOS, and Windows. The library is used to enable the Edge TPU runtime, which may heat up during operation. Google does not accept responsibility for any loss or damage if the device is operated outside the recommended ambient temperature range.

FlagPerf is an integrated AI hardware evaluation engine jointly built by the Institute of Intelligence and AI hardware manufacturers. It aims to establish an industry-oriented metric system to evaluate the actual capabilities of AI hardware under software stack combinations (model + framework + compiler). FlagPerf features a multidimensional evaluation metric system that goes beyond just measuring 'whether the chip can support specific model training.' It covers various scenarios and tasks, including computer vision, natural language processing, speech, multimodal, with support for multiple training frameworks and inference engines to connect AI hardware with software ecosystems. It also supports various testing environments to comprehensively assess the performance of domestic AI chips in different scenarios.

AI System School is a curated list of research in machine learning systems, focusing on ML/DL infra, LLM infra, domain-specific infra, ML/LLM conferences, and general resources. It provides resources such as data processing, training systems, video systems, autoML systems, and more. The repository aims to help users navigate the landscape of AI systems and machine learning infrastructure, offering insights into conferences, surveys, books, videos, courses, and blogs related to the field.

Multi-Agent Orchestrator is a flexible and powerful framework for managing multiple AI agents and handling complex conversations. It intelligently routes queries to the most suitable agent based on context and content, supports dual language implementation in Python and TypeScript, offers flexible agent responses, context management across agents, extensible architecture for customization, universal deployment options, and pre-built agents and classifiers. It is suitable for various applications, from simple chatbots to sophisticated AI systems, accommodating diverse requirements and scaling efficiently.

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.

AgentCloud is an open-source platform that enables companies to build and deploy private LLM chat apps, empowering teams to securely interact with their data. It comprises three main components: Agent Backend, Webapp, and Vector Proxy. To run this project locally, clone the repository, install Docker, and start the services. The project is licensed under the GNU Affero General Public License, version 3 only. Contributions and feedback are welcome from the community.

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