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.

The GPT Server project leverages the basic capabilities of FastChat to provide the capabilities of an openai server. It perfectly adapts more models, optimizes models with poor compatibility in FastChat, and supports loading vllm, LMDeploy, and hf in various ways. It also supports all sentence_transformers compatible semantic vector models, including Chat templates with function roles, Function Calling (Tools) capability, and multi-modal large models. The project aims to reduce the difficulty of model adaptation and project usage, making it easier to deploy the latest models with minimal code changes.

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。

HivisionIDPhoto is a practical algorithm for intelligent ID photo creation. It utilizes a comprehensive model workflow to recognize, cut out, and generate ID photos for various user photo scenarios. The tool offers lightweight cutting, standard ID photo generation based on different size specifications, six-inch layout photo generation, beauty enhancement (waiting), and intelligent outfit swapping (waiting). It aims to solve emergency ID photo creation issues.

TigerBot is a cutting-edge foundation for your very own LLM, providing a world-class large model for innovative Chinese-style contributions. It offers various upgrades and features, such as search mode enhancements, support for large context lengths, and the ability to play text-based games. TigerBot is suitable for prompt-based game engine development, interactive game design, and real-time feedback for playable games.

DISC-LawLLM is a legal domain large model that aims to provide professional, intelligent, and comprehensive **legal services** to users. It is developed and open-sourced by the Data Intelligence and Social Computing Lab (Fudan-DISC) at Fudan University.

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.

MEGREZ is a modern and elegant open-source high-performance computing platform that efficiently manages GPU resources. It allows for easy container instance creation, supports multiple nodes/multiple GPUs, modern UI environment isolation, customizable performance configurations, and user data isolation. The platform also comes with pre-installed deep learning environments, supports multiple users, features a VSCode web version, resource performance monitoring dashboard, and Jupyter Notebook support.

The Chinese LLM Benchmark is a continuous evaluation list of large models in CLiB, covering a wide range of commercial and open-source models from various companies and research institutions. It supports multidimensional evaluation of capabilities including classification, information extraction, reading comprehension, data analysis, Chinese encoding efficiency, and Chinese instruction compliance. The benchmark not only provides capability score rankings but also offers the original output results of all models for interested individuals to score and rank themselves.

Wenda is a platform for large-scale language model invocation designed to efficiently generate content for specific environments, considering the limitations of personal and small business computing resources, as well as knowledge security and privacy issues. The platform integrates capabilities such as knowledge base integration, multiple large language models for offline deployment, auto scripts for additional functionality, and other practical capabilities like conversation history management and multi-user simultaneous usage.

Go-stock is a tool for analyzing stock market data using the Go programming language. It provides functionalities for fetching stock data, performing technical analysis, and visualizing trends. With Go-stock, users can easily retrieve historical stock prices, calculate moving averages, and plot candlestick charts. This tool is designed to help investors and traders make informed decisions based on data-driven insights.

This repository, named 'awesome-ai-painting', is a comprehensive collection of resources related to AI painting. It is curated by a user named 秋风, who is an AI painting enthusiast with a background in the AIGC industry. The repository aims to help more people learn AI painting and also documents the user's goal of creating 100 AI products, with current progress at 4/100. The repository includes information on various AI painting products, tutorials, tools, and models, providing a valuable resource for individuals interested in AI painting and related technologies.

Awesome-ChatTTS is an official recommended guide for ChatTTS beginners, compiling common questions and related resources. It provides a comprehensive overview of the project, including official introduction, quick experience options, popular branches, parameter explanations, voice seed details, installation guides, FAQs, and error troubleshooting. The repository also includes video tutorials, discussion community links, and project trends analysis. Users can explore various branches for different functionalities and enhancements related to ChatTTS.