Confucius-o1-14B
The lightweight inference model developed by NetEase Youdao can be deployed on a single GPU and has o1-like inference capabilities.
Product Details
Confucius-o1-14B is an inference model developed by NetEase Youdao team and optimized based on Qwen2.5-14B-Instruct. It adopts a two-stage learning strategy that can automatically generate reasoning chains and summarize the step-by-step problem-solving process. This model is mainly oriented to the education field, and is especially suitable for answering K12 mathematics problems. It can help users quickly obtain correct problem-solving ideas and answers. The model is lightweight and can be deployed on a single GPU without quantization, lowering the threshold for use. Its reasoning capabilities have performed well in internal evaluations, providing strong technical support for AI applications in the education field.
Main Features
How to Use
Target Users
This product is mainly aimed at students, teachers and educational institutions in the field of education. Students can obtain detailed solution ideas and answers to mathematical problems through the model, improving learning efficiency; teachers can use the model to assist teaching and provide students with diversified problem-solving methods; educational institutions can integrate it into the teaching system to enrich teaching resources.
Examples
When students solve complex mathematical problems, they use this model to obtain detailed problem-solving ideas and step-by-step solution processes to better understand the problem.
When preparing lessons, teachers use models to generate a variety of problem-solving methods to enrich teaching content and improve classroom interactivity.
Educational institutions integrate the model into online learning platforms to provide students with personalized mathematics tutoring services.
Quick Access
Visit Website →Categories
Related Recommendations
Discover more similar quality AI tools
MegaTTS 3
MegaTTS 3 is an efficient speech synthesis model based on PyTorch developed by ByteDance, with ultra-high-quality speech cloning capabilities. Its lightweight architecture only contains 0.45B parameters, supports Chinese, English and code switching, can generate natural and smooth speech based on input text, and is widely used in academic research and technology development.
QVQ-Max
QVQ-Max is a visual reasoning model launched by the Qwen team, which can understand and analyze image and video content and provide solutions. It is not limited to text input, but can also handle complex visual information. Suitable for users who require multi-modal information processing, such as education, work and life scenarios. This product is developed based on deep learning and computer vision technology and is suitable for students, professionals and creative workers. This version is the first version and will continue to be optimized in the future.
Hugging Face Agents Course
🤗 AI Agents Course is a free online course provided by Hugging Face, designed to help learners grow from beginners to experts and master the theory, design and practice of AI agents. The course content is rich, covering many aspects from basic knowledge to practical applications. Through theoretical learning, practical operations and challenging tasks, it helps learners deeply understand the working principle of AI agents and learn to use the latest libraries and tools to build their own agents. The course also provides certification opportunities, where certificates can be obtained after completing specific tasks, suitable for students and developers interested in AI agents.
WebWalker
WebWalker is a multi-agent framework developed by Alibaba Group Tongyi Laboratory for evaluating the performance of large language models (LLMs) in web page traversal tasks. The framework systematically extracts high-quality data through exploration and evaluation paradigms by simulating the way humans browse the web. The main advantage of WebWalker lies in its innovative web page traversal capabilities, which can deeply mine multi-level information, making up for the shortcomings of traditional search engines in dealing with complex problems. This technology is of great significance for improving the performance of language models in open-domain question answering, especially in scenarios that require multi-step information retrieval. The development of WebWalker aims to promote the application and development of language models in the field of information retrieval.
SLM_Survey
SLM_Survey is a research project focused on Small Language Models (SLMs), aiming to provide in-depth understanding and technical evaluation of these models through surveys and measurements. The project covers Transformer-based, decoder-only language models with parameters ranging from 100M to 5B. By surveying 59 state-of-the-art open source SLMs, their technical innovations were analyzed, and their capabilities were evaluated in multiple areas, including common sense reasoning, contextual learning, mathematics, and programming. Additionally, their runtime costs, including inference latency and memory footprint, are benchmarked. These studies are of great value in promoting research in the field of SLMs.
o1 in Medicine
o1 in Medicine is an artificial intelligence model focused on the medical field, aiming to improve medical data processing capabilities and diagnostic accuracy through advanced language model technology. The model, developed by researchers at UC Santa Cruz, the University of Edinburgh, and the National Institutes of Health, demonstrated its potential for application in the medical field by testing on multiple medical data sets. The main advantages of the o1 model include high accuracy, multi-language support, and in-depth understanding of complex medical problems. The development background of this model is based on the current needs in the medical field for efficient and accurate data processing and analysis, especially in diagnosis and treatment recommendations. At present, the research and application of this model are still in the preliminary stage, but its application in medical education and clinical practice has broad prospects.
Interview pass
InterviewTong is an online tool that uses AI technology to provide interview assistance to job seekers. It helps users improve their interview success rate by accurately identifying interviewer questions in real time and generating professional answers. Product background is based on the challenges current candidates face during interview preparation, such as technical issues, behavioral issues, and stress interviews. Through its AI model, InterviewTong can provide instant and professional interview answers to help users perform well in interviews.
DeepLearning.AI
DeepLearning.AI is an online education platform founded by well-known artificial intelligence expert Andrew Ng. It focuses on providing high-quality courses and professional certificates in the fields of machine learning and deep learning. The platform provides beginners and professionals with a hands-on opportunity to learn AI skills and apply them. By partnering with industry leaders, DeepLearning.AI ensures that course content is cutting-edge and practical, helping learners build a solid foundation in the field of AI and advance their careers.
Zebra AI Science
Zebra AI Learning is a children's digital content product under Zebra Children's Science and Education Group, which uses AI technology to provide children with a personalized learning experience. The product has participated in the Ministry of Education's research projects and has more than 500 intellectual property rights, covering oral assessment, AI interaction and other aspects, aiming to comprehensively cultivate children's core competencies and all-round abilities.
Anthropic's Prompt Engineering Interactive Tutorial
Anthropic's interactive tutorials are designed to provide comprehensive step-by-step guidance on how to build optimal prompts within Claude. The tutorial consists of 9 chapters, each with exercises, and an appendix containing more advanced methods. The tutorial uses Anthropic's smallest, fastest, and cheapest model, the Claude 3 Haiku, as well as the smarter Claude 3 Sonnet and Claude 3 Opus models. The tutorial also exists on Google Sheets using Anthropic's Claude for Sheets extension, which version is recommended as it is more user-friendly.
NuminaMath
NuminaMath is a set of databases and models designed for training state-of-the-art mathematical language models (SOTA math LLMs). It contains 860k+ math competition questions and their solution pairs, each solution is templated using Chain of Thought (CoT) reasoning. In addition, there are 70k+ math competition problems whose solutions are generated by GPT-4 through Tool-Integrated Reasoning (TIR). NuminaMath provides educators and students with a valuable resource that helps them improve their mathematical thinking and problem-solving skills by providing high-quality mathematical problems and solutions.
RWKV
RWKV is an innovative deep learning architecture that combines the best features of RNN and Transformer. It provides excellent performance, fast inference and training, does not rely on self-attention mechanisms, saves VRAM, and supports 'unlimited' context lengths. RWKV performs well in multiple languages and encodings, becoming a popular choice among developers around the world and driving the advancement of open source large language models.
AI math problem solver
AI Math Problem Solver is an online tool powered by Math AI and Math GPT models (such as GPT-4o), designed to provide solutions to a wide range of math problems. It leverages advanced artificial intelligence technology to provide students and teachers with detailed step-by-step solutions that enhance understanding of mathematical concepts and problem-solving skills. The background of this product is the demand for efficient problem-solving tools in mathematics learning, and it is positioned to provide high-quality educational support for free.
Eureka Labs
Eureka Labs is committed to building a new AI-native school that provides personalized, high-quality teaching assistance by combining generative AI technology, aiming to achieve broad coverage of educational resources and in-depth learning. Its first product, LLM101n, is an undergraduate-level course that guides students in training their own AI, similar to a scaled-down version of an AI teaching assistant. Course materials are available online, and there are plans to run both digital and physical course groups.
Study with GPT
Study with GPT is an online education platform that uses artificial intelligence technology to provide users with personalized technical learning solutions. The platform can design tutorial outlines, explain knowledge points according to users' learning goals, and provide 24-hour online Q&A services. It is mainly intended for individual users who want to improve their programming skills, especially beginners and self-learners. The main advantages of the platform include personalized learning plans, endless patient explanations, 24-hour online support, and the convenience of not having to search for tutorial resources everywhere.
Learn RAG with Langchain
Retrieval-Augmented Generation (RAG) is a cutting-edge technology that enhances the capabilities of generative models by integrating external knowledge sources to improve the quality and reliability of generated content. LangChain is a powerful framework designed for building and deploying robust language model applications. This tutorial series will provide a comprehensive, step-by-step guide to help you implement RAG using LangChain, starting from the introduction of the basic RAG process, gradually deepening into query transformation, document embedding, routing mechanism, query construction, index strategy, retrieval technology and generation stage, and finally integrating all concepts into a practical scenario to demonstrate the power and flexibility of RAG.