Transformers

Running HuggingFace DeepSeek V2 on Single Node A800

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Running HuggingFace DeepSeek V2 on Single Node A800

0x0. Background Trying to run the DeepSeek V2 released on HuggingFace, I encountered several issues. Here are the solutions. The open-source DeepSeek V2 repo link provided by HuggingFace is: https://huggingface.co/deepseek-ai/DeepSeek-V2 0x1. Error 1: KeyError: ‘sdpa’ This issue has also been reported by the community. https://huggingface.co/deepseek-ai/DeepSeek-V2/discussions/3 Insert image description here The solution is quite simple; just … Read more

Getting Started with Hugging Face

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Getting Started with Hugging Face

This Article Covers The main contents of this article include: What is Hugging Face and what does it offer Using Hugging Face models (Transformers library) Using Hugging Face datasets (Datasets library) Introduction to Hugging Face Similar to GitHub, Hugging Face is a hub (community). It can be considered the GitHub of the machine learning world. … Read more

Detailed Explanation of HuggingFace BERT Source Code

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Detailed Explanation of HuggingFace BERT Source Code

Follow the official account “ML_NLP“ Set as “Starred“, heavy content delivered first-hand! Reprinted from | PaperWeekly ©PaperWeekly Original · Author | Li Luoqiu School | Master’s Student at Zhejiang University Research Direction | Natural Language Processing, Knowledge Graphs This article records my understanding of the code in the HuggingFace open-source Transformers project. As we all … Read more

Understanding Transformers: A Comprehensive Guide

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Understanding Transformers: A Comprehensive Guide

This article is the first in a series produced by Big Data Digest and Baidu NLP. Baidu NLP is committed to the mission of “understanding language, possessing intelligence, and changing the world”. It conducts technical research and product applications in areas including natural language processing, machine learning, and data mining, leading the development of artificial … Read more

Unveiling the Mathematical Principles of Transformers

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Unveiling the Mathematical Principles of Transformers

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

Where Does the Context Learning Ability of Transformers Come From?

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Where Does the Context Learning Ability of Transformers Come From?

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

Attention Mechanism in Deep Learning

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Attention Mechanism in Deep Learning

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

Understanding the Principles Behind AgentGPT

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Understanding the Principles Behind AgentGPT

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

Local Invocation of Llama3 Large Model Development

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Local Invocation of Llama3 Large Model Development

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

Natural Language Processing in Python: 5 Useful Libraries!

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Natural Language Processing in Python: 5 Useful Libraries!

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