In October 2018, the BERT model launched by Google made a stunning impact, sweeping various rankings and even surpassing human baseline scores, achieving a milestone breakthrough in the field of NLP. Today, for NLP algorithm engineers, BERT has become an essential tool. “What if there’s too little data?” — “Just fine-tune BERT!” “What if RNN … Read more
Author: Dr. Zhang Junlin, Senior Algorithm Expert at Sina Weibo Zhihu Column:Notes on the Frontiers of Deep Learning This article has been authorized, you can click “Read the original” at the end of the article to go directly: https://zhuanlan.zhihu.com/p/65470719 In the past two months, I have been paying close attention to the current application status … Read more
Author: Su Jianlin Research Direction: NLP, Neural Networks Personal Homepage: kexue.fm Bert is something that probably doesn’t need much introduction. Although I’m not a big fan of Bert, I must say it has indeed caused quite a stir in the NLP community. Nowadays, whether in Chinese or English, there is a plethora of popular science … 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 community’s vision is to promote communication and progress between the academic and industrial sectors of natural language processing and machine learning, especially for beginners. Reprinted from | … Read more
↑ ClickBlue Text Follow the Jishi platform Author丨Jarvis73@Zhihu (Authorized) Source丨https://zhuanlan.zhihu.com/p/601905339 Editor丨Jishi Platform Jishi Guide This article summarizes Prompt Learning/Tuning. >> Join the Jishi CV technology exchange group to stay at the forefront of computer vision Since the advent of Self-Attention and Transformer, they have become the new stars in the field of natural language processing. … Read more
“ As a non-professional beginner, my initial interest in large models led me to explore related knowledge. As I read more papers and reports, I always wanted to practice with large models but didn’t know where to start. I believe many students share the same experience as I did back then. This article will guide … Read more
1. Basic Description 1. The models from ollama are all stripped-down versions. 2. They are relatively weak in distributed environments. 3. ollama is lightweight and suitable for individual users, but not for enterprise users. 4. ollama only performs model inference (used for running models). 5. ollama uses GGUF for quantization. 6. Engineering deployment generally does … Read more
1. RAG (Retrieval Augmented Generation) RAG technology is a method that combines retrieval and generation. It typically relies on two core components: a large language model (such as GPT-3) and a retrieval system (such as a vector database). RAG first uses the retrieval system to extract relevant information from a vast amount of data, then … Read more
Warning: This may be the easiest to understand, easiest to run example for efficient fine-tuning of various open-source LLM models, supporting both multi-turn and single-turn dialogue datasets. We constructed a toy dataset of three rounds of dialogue that modifies the self-awareness of the large model, using the QLoRA algorithm, which can complete fine-tuning in just … Read more
01 Introduction On August 3, Alibaba Cloud released its first open-source large model: Tongyi Qwen-7B, which is open-source and commercially usable. Although everyone has been raised expectations with various hundred-billion parameter models, the fact that it is produced by Alibaba has attracted widespread attention and discussion among peers, and it has performed excellently on various … Read more