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Immersive Translate is a highly rated bilingual translation website extension that offers free translation services for foreign language websites, PDF documents, EPUB eBooks, and video subtitles. It allows users to select from various artificial intelligence engines like OpenAI (ChatGPT), DeepL, and Gemini for translation. The extension intelligently identifies main content areas of web pages for bilingual translations, supports real-time bilingual subtitle translations on major video platforms, and introduces innovative features for PDF and EPUB translation. Immersive Translate aims to break down language barriers and promote information equity by providing professional translation results with just one click.

Magic Prompt is a website that provides users with a collection of AI-generated image prompts. Users can search for prompts by keyword or browse through a variety of categories. The website also includes a tool that allows users to generate their own prompts. Magic Prompt is a valuable resource for anyone looking to create unique and interesting AI-generated images.

LLM-Finetune is a repository for fine-tuning language models for various NLP tasks such as text classification and named entity recognition. It provides instructions and scripts for training and inference using models like Qwen2-VL and GLM4. The repository also includes datasets for tasks like text classification, named entity recognition, and multimodal tasks. Users can easily prepare the environment, download datasets, train models, and perform inference using the provided scripts and notebooks. Additionally, the repository references SwanLab, an AI training record, analysis, and visualization tool.

This repository provides a detailed tutorial on how to build your own large language model (LLM) from scratch. It includes all the code necessary to create a GPT-like LLM, covering the encoding, pre-training, and fine-tuning processes. The tutorial is written in a clear and concise style, with plenty of examples and illustrations to help you understand the concepts involved. It is suitable for developers and researchers with some programming experience who are interested in learning more about LLMs and how to build them.

The second edition of "PyTorch Practical Tutorial" was completed after 5 years, 4 years, and 2 years. On the basis of the essence of the first edition, rich and detailed deep learning application cases and reasoning deployment frameworks have been added, so that this book can more systematically cover the knowledge involved in deep learning engineers. As the development of artificial intelligence technology continues to emerge, the second edition of "PyTorch Practical Tutorial" is not the end, but the beginning, opening up new technologies, new fields, and new chapters. I hope to continue learning and making progress in artificial intelligence technology with you in the future.

LLMLanding is a repository focused on practical implementation of large models, covering topics from theory to practice. It provides a structured learning path for training large models, including specific tasks like training 1B-scale models, exploring SFT, and working on specialized tasks such as code generation, NLP tasks, and domain-specific fine-tuning. The repository emphasizes a dual learning approach: quickly applying existing tools for immediate output benefits and delving into foundational concepts for long-term understanding. It offers detailed resources and pathways for in-depth learning based on individual preferences and goals, combining theory with practical application to avoid overwhelm and ensure sustained learning progress.

This project open sources the **Chinese LLaMA model and the Alpaca large model fine-tuned with instructions**, to further promote the open research of large models in the Chinese NLP community. These models **extend the Chinese vocabulary based on the original LLaMA** and use Chinese data for secondary pre-training, further enhancing the basic Chinese semantic understanding ability. At the same time, the Chinese Alpaca model further uses Chinese instruction data for fine-tuning, significantly improving the model's understanding and execution of instructions.

Analyze the following text from a github repository (name and readme text at end) . Then, generate a JSON object with the following keys and provide the corresponding information for each key, ,'for_jobs' (List 5 jobs suitable for this tool,in lowercase letters), 'ai_keywords' (keywords of the tool,in lowercase letters), 'for_tasks' (list of 5 specific tasks user can use this tool to do,in less than 3 words,Verb + noun form,in daily spoken language,in lowercase letters).Answer in english languagesname:Awesome-Chinese-LLM readme:# Awesome Chinese LLM   An Awesome Collection for LLM in Chinese 收集和梳理中文LLM相关    自ChatGPT为代表的大语言模型（Large Language Model, LLM）出现以后，由于其惊人的类通用人工智能（AGI）的能力，掀起了新一轮自然语言处理领域的研究和应用的浪潮。尤其是以ChatGLM、LLaMA等平民玩家都能跑起来的较小规模的LLM开源之后，业界涌现了非常多基于LLM的二次微调或应用的案例。本项目旨在收集和梳理中文LLM相关的开源模型、应用、数据集及教程等资料，目前收录的资源已达100+个！ 如果本项目能给您带来一点点帮助，麻烦点个⭐️吧～ 同时也欢迎大家贡献本项目未收录的开源模型、应用、数据集等。提供新的仓库信息请发起PR，并按照本项目的格式提供仓库链接、star数，简介等相关信息，感谢~

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.

AI By Doing is a hands-on artificial intelligence tutorial series that aims to help beginners understand the principles of machine learning and deep learning while providing practical applications. The content covers various supervised and unsupervised learning algorithms, machine learning engineering, deep learning fundamentals, frameworks like TensorFlow and PyTorch, and applications in computer vision and natural language processing. The tutorials are written in Jupyter Notebook format, combining theory, mathematical derivations, and Python code implementations to facilitate learning and understanding.

Taiyi (太一) is a bilingual large language model fine-tuned for diverse biomedical tasks. It aims to facilitate communication between healthcare professionals and patients, provide medical information, and assist in diagnosis, biomedical knowledge discovery, drug development, and personalized healthcare solutions. The model is based on the Qwen-7B-base model and has been fine-tuned using rich bilingual instruction data. It covers tasks such as question answering, biomedical dialogue, medical report generation, biomedical information extraction, machine translation, title generation, text classification, and text semantic similarity. The project also provides standardized data formats, model training details, model inference guidelines, and overall performance metrics across various BioNLP tasks.

This repository is a Chinese translation of the GitHub project 'LLMs-from-scratch', including detailed markdown notes and related Jupyter code. The translation process aims to maintain the accuracy of the original content while optimizing the language and expression to better suit Chinese learners' reading habits. The repository features detailed Chinese annotations for all Jupyter code, aiding users in practical implementation. It also provides various supplementary materials to expand knowledge. The project focuses on building Large Language Models (LLMs) from scratch, covering fundamental constructions like Transformer architecture, sequence modeling, and delving into deep learning models such as GPT and BERT. Each part of the project includes detailed code implementations and learning resources to help users construct LLMs from scratch and master their core technologies.

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。

Train-llm-from-scratch is a repository that guides users through training a Large Language Model (LLM) from scratch. The model size can be adjusted based on available computing power. The repository utilizes deepspeed for distributed training and includes detailed explanations of the code and key steps at each stage to facilitate learning. Users can train their own tokenizer or use pre-trained tokenizers like ChatGLM2-6B. The repository provides information on preparing pre-training data, processing training data, and recommended SFT data for fine-tuning. It also references other projects and books related to LLM training.

AI tag generator that combines 40,000 tags from Bilibili UP main Twelve Today is also very cute with Chinese translations from Novelai, providing Chinese search and tag generation services. It offers a tag community for magicians to directly copy and generate spells. Always free, no ads, no commercial use. The project includes a pure tag parsing library, independent spell parsing library, tag data repository, and a new gallery page with waterfall flow for viewing community 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.

Large Language Models (LLM) are language models built by deep neural networks containing hundreds of billions of weights, trained on a large amount of unlabeled text using self-supervised learning methods. Since 2018, companies and research institutions including Google, OpenAI, Meta, Baidu, and Huawei have released various models such as BERT, GPT, etc., which have performed well in almost all natural language processing tasks. Starting in 2021, large models have shown explosive growth, especially after the release of ChatGPT in November 2022, attracting worldwide attention. Users can interact with systems using natural language to achieve various tasks from understanding to generation, including question answering, classification, summarization, translation, and chat. Large language models demonstrate powerful knowledge of the world and understanding of language. This repository introduces the basic theory of large language models including language models, distributed model training, and reinforcement learning, and uses the Deepspeed-Chat framework as an example to introduce the implementation of large language models and ChatGPT-like systems.

Awesome-AGI is a curated list of resources related to Artificial General Intelligence (AGI), including models, pipelines, applications, and concepts. It provides a comprehensive overview of the current state of AGI research and development, covering various aspects such as model training, fine-tuning, deployment, and applications in different domains. The repository also includes resources on prompt engineering, RLHF, LLM vocabulary expansion, long text generation, hallucination mitigation, controllability and safety, and text detection. It serves as a valuable resource for researchers, practitioners, and anyone interested in the field of AGI.

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.

AIAS is a comprehensive AI training platform that offers courses and practical examples in various AI fields such as traditional image processing, deep learning algorithms, JavaAI applications, NLP, web development, image generation, and desktop application development. The platform also provides SDKs for tasks like image recognition, OCR, natural language processing, audio processing, video analysis, and big data analysis. Users can access training materials, source code, and tools for developing AI applications across different domains.

This repository contains a collection of papers and code for the CVPR 2024 conference. The papers cover a wide range of topics in computer vision, including object detection, image segmentation, image generation, and video analysis. The code provides implementations of the algorithms described in the papers, making it easy for researchers and practitioners to reproduce the results and build upon the work of others. The repository is maintained by a team of researchers at the University of California, Berkeley.

长文本分拆，以满足碎片化学习的需要

输入英文哲学文本，输出中文翻译

中文文本填充专家

模仿给定文本的语言风格和表述习惯

我会将复杂的文本信息提炼成流程化的表达，辅助你快速了解一个流程。

专业翻译，确保准确性与文本效果提升。

帮你创作丰富文本内容的个人成长和职业发展作家

专注于从剧本文本提取高光片段

分析用户输入的文本，帮他们明晰需求

直接输入文本，将任意语言翻译成流畅日语

将视频字幕文本整理为逐字稿

美创冒险记是一个基于文本的角色扮演游戏，玩家将扮演博物馆展览公司的工作人员，解决实际工作中的难题。MGCDIGI Adventures is a role-playing game where players play as museum exhibition designers.

扮演末日蟑螂中的角色，也可自己上传文本

一个帮助用户写Prompt的专业助手，支持各种模型的文本生视频 Sora｜Pika｜Gen-2 By 内啡肽

专注于理解和解释人类语言，常使用这些算法来分析文本数据。

高质量，日语母语者，能精准理解中文的含义，可提供自然的日语表达，可以精准确认到日语的语法错误，可提供高质量文本翻译与优化

专注小红书笔记写作，有了它你也可以是小红书爆款写作专家！