Machine Heart Reports Editor: Zhao Yang Recently, a paper was published on arXiv, providing a new interpretation of the mathematical principles behind Transformers. The content is extensive and rich in knowledge, and I highly recommend reading the original. In 2017, Vaswani et al. published “Attention Is All You Need,” marking a significant milestone in the … Read more
Machine Heart reports Machine Heart Editorial Department With a theoretical foundation, we can perform deep optimization. Why is the performance of transformers so good? Where does the context learning (In-Context Learning) ability it brings to many large language models come from? In the field of artificial intelligence, transformers have become the dominant model in deep … Read more
Introduction Alexander J. Smola, the head of machine learning at Amazon Web Services, presented on the attention mechanism in deep learning at the ICML2019 conference, detailing the evolution from the earliest Nadaraya-Watson Estimator (NWE) to the latest Multiple Attention Heads. Authors | Alex Smola, Aston Zhang Translator | Xiaowen The report is divided into six … Read more
Start a new objective: analyze the principles of AgentGPT and summarize the results. New task: research the development and architecture of the GPT model. New task: analyze the internal processes and algorithms of AgentGPT. New task: summarize the investigation results and submit a comprehensive report on the principles behind AgentGPT. Executing “Research the development of … Read more
1. Test using the trained weights from transformers import AutoModelForCausalLM,AutoTokenizer,TextGenerationPipeline import torch tokenizer = AutoTokenizer.from_pretrained(r"E:\大模型AI开发\AI大模型\projects\gpt2\model\models–uer–gpt2-chinese-cluecorpussmall\snapshots\c2c0249d8a2731f269414cc3b22dff021f8e07a3") model = AutoModelForCausalLM.from_pretrained(r"E:\大模型AI开发\AI大模型\projects\gpt2\model\models–uer–gpt2-chinese-cluecorpussmall\snapshots\c2c0249d8a2731f269414cc3b22dff021f8e07a3") # Load our own trained weights (Chinese poetry) model.load_state_dict(torch.load("net.pt")) # Use the system's built-in pipeline tool to generate content pipline = TextGenerationPipeline(model,tokenizer,device=0) print(pipline("天高", max_length=24)) The performance is actually not good: 2. Post-process the AI-generated results # Customized … Read more
Hello everyone, I’m Hao! Today I will introduce 10 incredibly useful Python tools for Natural Language Processing (NLP). As a Python developer, I understand how important it is to have a handy tool when dealing with text data. These tools not only help us better understand and analyze text but also make our work much … Read more
Hello everyone! I am Hao Ge. Today I want to share with you a particularly interesting topic – Natural Language Processing in Python. Simply put, it is the technology that allows computers to understand and process human language. As a Python enthusiast, I find that many friends are particularly interested in this field. Below, I … Read more
Attention Mechanisms have become the foundational architecture for model design; it’s almost a given that a good model should incorporate some form of attention. This article summarizes the current state of Attention Mechanisms by introducing 17 mainstream types of attention mechanisms, explaining their basic principles and computational methods, and providing their sources along with corresponding … Read more