Best AI tools for< 语言模型工程师 >
Infographic
4 - AI tool Sites
ChatMoneyAI
ChatMoneyAI is an AI application designed for AI monetization. It offers various AI solutions for businesses across different industries, including AI chat systems, AI drawing systems, AI interface integration, and training large language models. The application aims to empower enterprises with digital and intelligent transformation through its advanced technology, private deployment options, personalized customization, and stable performance. ChatMoneyAI has been widely adopted in multiple industries, serving as a reliable partner for businesses seeking efficiency enhancement and cost reduction.
SpeakAI
SpeakAI is an immersive language learning app powered by AI. With its AI assistant, multi-language support, and interactive exercises, SpeakAI provides a personalized learning experience tailored to your needs and pace. Learn Chinese, English, Japanese, Korean, French, German, Italian, and Spanish through engaging scenario-based lessons, real-time grammar correction, and a wide range of voice options. Start your language learning journey today with SpeakAI!
包阅AI
包阅AI is an intelligent AI reading assistant that covers various scenarios such as paper reading, legal analysis, scientific research, marketing, education, brand analysis, and business understanding. It supports multiple document formats like PDF, Word, PPT, EPUB, Mobi, TXT, and Markdown. The tool offers features like document interpretation, web page summarization, contract review, resume analysis, and financial document analysis. With the ability to analyze over 50,000 documents and assist more than 100,000 knowledge workers efficiently, it aims to enhance work and study productivity through AI-powered assistance.
Immersive Translate
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.
20 - Open Source Tools
Llama-Chinese
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。
get_jobs
Get Jobs is a tool designed to help users find and apply for job positions on various recruitment platforms in China. It features AI job matching, automatic cover letter generation, multi-platform job application, automated filtering of inactive HR and headhunter positions, real-time WeChat message notifications, blacklisted company updates, driver adaptation for Win11, centralized configuration, long-lasting cookie login, XPathHelper plugin, global logging, and more. The tool supports platforms like Boss直聘, 猎聘, 拉勾, 51job, and 智联招聘. Users can configure the tool for customized job searches and applications.
awesome-LLM-resourses
A comprehensive repository of resources for Chinese large language models (LLMs), including data processing tools, fine-tuning frameworks, inference libraries, evaluation platforms, RAG engines, agent frameworks, books, courses, tutorials, and tips. The repository covers a wide range of tools and resources for working with LLMs, from data labeling and processing to model fine-tuning, inference, evaluation, and application development. It also includes resources for learning about LLMs through books, courses, and tutorials, as well as insights and strategies from building with LLMs.
llm-universe
This project is a tutorial on developing large model applications for novice developers. It aims to provide a comprehensive introduction to large model development, focusing on Alibaba Cloud servers and integrating personal knowledge assistant projects. The tutorial covers the following topics: 1. **Introduction to Large Models**: A simplified introduction for novice developers on what large models are, their characteristics, what LangChain is, and how to develop an LLM application. 2. **How to Call Large Model APIs**: This section introduces various methods for calling APIs of well-known domestic and foreign large model products, including calling native APIs, encapsulating them as LangChain LLMs, and encapsulating them as Fastapi calls. It also provides a unified encapsulation for various large model APIs, such as Baidu Wenxin, Xunfei Xinghuo, and Zh譜AI. 3. **Knowledge Base Construction**: Loading, processing, and vector database construction of different types of knowledge base documents. 4. **Building RAG Applications**: Integrating LLM into LangChain to build a retrieval question and answer chain, and deploying applications using Streamlit. 5. **Verification and Iteration**: How to implement verification and iteration in large model development, and common evaluation methods. The project consists of three main parts: 1. **Introduction to LLM Development**: A simplified version of V1 aims to help beginners get started with LLM development quickly and conveniently, understand the general process of LLM development, and build a simple demo. 2. **LLM Development Techniques**: More advanced LLM development techniques, including but not limited to: Prompt Engineering, processing of multiple types of source data, optimizing retrieval, recall ranking, Agent framework, etc. 3. **LLM Application Examples**: Introduce some successful open source cases, analyze the ideas, core concepts, and implementation frameworks of these application examples from the perspective of this course, and help beginners understand what kind of applications they can develop through LLM. Currently, the first part has been completed, and everyone is welcome to read and learn; the second and third parts are under creation. **Directory Structure Description**: requirements.txt: Installation dependencies in the official environment notebook: Notebook source code file docs: Markdown documentation file figures: Pictures data_base: Knowledge base source file used
llm-cookbook
LLM Cookbook is a developer-oriented comprehensive guide focusing on LLM for Chinese developers. It covers various aspects from Prompt Engineering to RAG development and model fine-tuning, providing guidance on how to learn and get started with LLM projects in a way suitable for Chinese learners. The project translates and reproduces 11 courses from Professor Andrew Ng's large model series, categorizing them for beginners to systematically learn essential skills and concepts before exploring specific interests. It encourages developers to contribute by replicating unreproduced courses following the format and submitting PRs for review and merging. The project aims to help developers grasp a wide range of skills and concepts related to LLM development, offering both online reading and PDF versions for easy access and learning.
self-llm
This project is a Chinese tutorial for domestic beginners based on the AutoDL platform, providing full-process guidance for various open-source large models, including environment configuration, local deployment, and efficient fine-tuning. It simplifies the deployment, use, and application process of open-source large models, enabling more ordinary students and researchers to better use open-source large models and helping open and free large models integrate into the lives of ordinary learners faster.
Awesome-LLM-Eval
Awesome-LLM-Eval: a curated list of tools, benchmarks, demos, papers for Large Language Models (like ChatGPT, LLaMA, GLM, Baichuan, etc) Evaluation on Language capabilities, Knowledge, Reasoning, Fairness and Safety.
PyTorch-Tutorial-2nd
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.
llm-action
This repository provides a comprehensive guide to large language models (LLMs), covering various aspects such as training, fine-tuning, compression, and applications. It includes detailed tutorials, code examples, and explanations of key concepts and techniques. The repository is maintained by Liguo Dong, an AI researcher and engineer with expertise in LLM research and development.
DecryptPrompt
This repository does not provide a tool, but rather a collection of resources and strategies for academics in the field of artificial intelligence who are feeling depressed or overwhelmed by the rapid advancements in the field. The resources include articles, blog posts, and other materials that offer advice on how to cope with the challenges of working in a fast-paced and competitive environment.
modern_ai_for_beginners
This repository provides a comprehensive guide to modern AI for beginners, covering both theoretical foundations and practical implementation. It emphasizes the importance of understanding both the mathematical principles and the code implementation of AI models. The repository includes resources on PyTorch, deep learning fundamentals, mathematical foundations, transformer-based LLMs, diffusion models, software engineering, and full-stack development. It also features tutorials on natural language processing with transformers, reinforcement learning, and practical deep learning for coders.
AIProductHome
AI Product Home is a repository dedicated to collecting various AI commercial or open-source products. It provides assistance in submitting issues, self-recommendation, correcting resources, and more. The repository also features AI tools like Build Naidia, Autopod, Rytr, Mubert, and a virtual town driven by AI. It includes sections for AI models, chat dialogues, AI assistants, code assistance, artistic creation, content creation, and more. The repository covers a wide range of AI-related tools and resources for users interested in AI products and services.
Awesome-AGI
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.
awesome-cuda-tensorrt-fpga
Okay, here is a JSON object with the requested information about the awesome-cuda-tensorrt-fpga repository:
how-to-optim-algorithm-in-cuda
This repository documents how to optimize common algorithms based on CUDA. It includes subdirectories with code implementations for specific optimizations. The optimizations cover topics such as compiling PyTorch from source, NVIDIA's reduce optimization, OneFlow's elementwise template, fast atomic add for half data types, upsample nearest2d optimization in OneFlow, optimized indexing in PyTorch, OneFlow's softmax kernel, linear attention optimization, and more. The repository also includes learning resources related to deep learning frameworks, compilers, and optimization techniques.
20 - OpenAI Gpts
语言大师 Linguistic Composer
Creates sentences from words with English-Chinese translations and analyses.
Translate Any Language to Chinese 翻译任何语言到中文
🇬🇧🇪🇸🇫🇷🇩🇪🇷🇺🇯🇵→🇨🇳 Just input your original text.只需要直接输入你需要翻译的文字。
Chinese 智译
无需说明,自动在中文和其他语言间互译,支持翻译代码注释、文言文、文档文件以及图片。No need for explanations, automatically translate between Chinese and other languages, support translation of code comments, classical Chinese, document files, and images.