Pinned Repositories
A-tutorial-compiler-written-in-Java
For Jack language. Most of codes were commented with their usage, which can be useful for beginner to realize the running principle of a compiler for object-oriented programming language.
AI-Optimizer
The next generation deep reinforcement learning tookit
app-monorepo
🔑 The world's first truly open source crypto wallet runs on all platforms: iOS, Android, Windows, macOS, Linux, Chrome, Firefox... and more. Follow up to get updated.
AudioLCM
PyTorch Implementation of AudioLCM (ACM-MM'24): a efficient and high-quality text-to-audio generation with latent consistency model.
awesome-game-security
awesome game security [Welcome to PR]
bisheng
Bisheng is an open LLM devops platform for next generation AI applications.
bmp180
BMP180 full function driver for general MCU and Linux.
Explorer
Remember
go-zero
A cloud-native Go microservices framework with cli tool for productivity.
PolarisK's Repositories
PolarisK/AudioLCM
PyTorch Implementation of AudioLCM (ACM-MM'24): a efficient and high-quality text-to-audio generation with latent consistency model.
PolarisK/bisheng
Bisheng is an open LLM devops platform for next generation AI applications.
PolarisK/bmp180
BMP180 full function driver for general MCU and Linux.
PolarisK/DAMO-YOLO
DAMO-YOLO: a fast and accurate object detection method with some new techs, including NAS backbones, efficient RepGFPN, ZeroHead, AlignedOTA, and distillation enhancement.
PolarisK/CSGHub
CSGHub is an opensource large model assets platform just like on-premise huggingface which helps to manage datasets, model files, codes and more. CSGHub是一个开源、可信的大模型资产管理平台，可帮助用户治理LLM和LLM应用生命周期中涉及到的资产（数据集、模型文件、代码等）。CSGHub提供类似私有化的Huggingface功能，以类似OpenStack Glance管理虚拟机镜像、Harbor管理容器镜像以及Sonatype Nexus管理制品的方式，实现对LLM资产的管理。欢迎关注反馈和Star⭐️
PolarisK/Deep-RL-Notes
A collection of comprehensive notes on Deep Reinforcement Learning, customized for UC Berkeley's CS 285 (prev. CS 294-112)
PolarisK/DeepBI
LLM based data scientist, AI native data application. AI-driven infinite thinking redefines BI.
PolarisK/Developer
Developer
PolarisK/dgiot
Open source platform for iot , 6 min Quick Deployment,10M devices connection,Carrier level Stability;物联网开源平台,6分钟快速部署,千万级承载,电信级稳定性. Low code for Object model-Rule Engine-Data Channel-Configuration Page; Fully open source, Multi industrial protocols are compatible.物模型-规则引擎-数据通道-组态页面全流程零代码开发、全业务全链路模拟压测、前后端完全开源、多行业工业协议兼容。QQ群:346566935
PolarisK/duix.ai
PolarisK/hs_tracker
An automatic Hearthstone tracker for Android
PolarisK/HummerRisk
HummerRisk 是云原生安全平台，包括混合云安全治理和容器云安全检测。
PolarisK/labilibili
仿bilibili前后端实现
PolarisK/max6675
MAX6675 full function driver for general MCU and Linux.
PolarisK/MetaLowCode
MetaLowCode——美乐低代码，企业级全栈低代码开发平台。私有部署，源码交付。
PolarisK/mpu6050
mpu6050 full function driver
PolarisK/OmDet
Real-time and accurate open-vocabulary end-to-end object detection
PolarisK/orillusion
Orillusion is a pure Web3D rendering engine which is fully developed based on the WebGPU standard.
PolarisK/Owl
A Large Language Model for IT Operations
PolarisK/PromptTuning2MetonymyResolution
How many surprises it can bring us when adapting the Prompt Learning paradigm to the Metonymy Resolution task?
PolarisK/reactuse
Collection of essential React Hooks Utilities.
PolarisK/scalpel
scalpel是一款命令行漏洞扫描工具，支持深度参数注入，拥有一个强大的数据解析和变异算法，可以将常见的数据格式（json, xml, form等）解析为树结构，然后根据poc中的规则，对树进行变异，包括对叶子节点和树结构 的变异。变异完成之后，将树结构还原为原始的数
PolarisK/SQLAgent
SQLAgent 是一个 开源的（Open source）、大模型驱动的（LLM-Powered）、专注于私有化部署的Text2SQL 智能体（Agent） 项目（Project）
PolarisK/synthetic-data-generator
SDG is a component focused on the rapid generation of synthetic data for structured tabular data.
PolarisK/transagents
PolarisK/TravelGPT
PolarisK/UFO
A UI-Focused Agent for Windows OS Interaction.
PolarisK/websoft9
Websoft9 is web-based PaaS/Linux Panel for running open source
PolarisK/xxim-server
惺惺 —— 属于你的社交地盘！惺惺是一个100%开源社交平台，每个人都可以搭建自己的服务器，掌握数据的所有权。此APP非盈利项目！
PolarisK/YiVal
🚀 Evaluate and Evolve.🚀 YiVal is an open source GenAI-Ops framework that allows you to manually or automatically tune and evaluate your AIGC prompts, retrieval configs and fine-tune the model params all at once with your preferred choices of test dataset generation, evaluation algorithms and improvement strategies.