Approximately 3500 words, recommended reading time 10 minutes. Today, we will explore the core technologies behind large models! 1. Transformer The Transformer model is undoubtedly the solid foundation of large language models, ushering in a new era in deep learning. In the early stages, Recurrent Neural Networks (RNNs) were the core means of handling sequential … Read more
MLNLP community is a well-known machine learning and natural language processing community both domestically and internationally, covering NLP graduate and doctoral students, university teachers, and corporate researchers. The Vision of the Community is to promote communication and progress between the academic and industrial circles of natural language processing and machine learning, especially for beginners. Reprinted … Read more
The author is from Meta, an internet practitioner, focusing on LLM4Code and LLMinfra. The original text is from Zhihu, link: https://zhuanlan.zhihu.com/p/16270225772 This article is for academic/technical sharing only. If there is any infringement, please contact for removal. RLHF is an important part of LLM training. With the development of open-source models, we observe that some … Read more
Introduction I must say, Qwen is really impressive. It seems that its foundational capabilities have firmly established it as the leader in open source, and it is not at all inferior compared to most closed sources. Many companies’ foundational teams are likely already being judged on the significance of foundational models. Qwen’s open-source momentum is … Read more
Source | Eye on AIOneFlow Compilation and Translation | Jia Chuan, Yang Ting, Xu Jiayu “The hallucination problem of ‘serious nonsense’ is a common issue that large language models (LLMs) like ChatGPT urgently need to address. Although reinforcement learning from human feedback (RLHF) can adjust the model’s output for errors, it is not efficient or … Read more