【Coding】Hugging Face BertModel中的一些类和参数说明整理(三) 继续上一篇的past_key_values问题,梳理一下BertLayer中的attention,以及past_key_values怎么使用,首先我们在使用from_pretrained 加载bert模型,比如bert-uncased这类模型时,只是单纯的bert,没有涉及到seq2seq和ge
首先,要明确的是past_key_value是注意力机制中用到的,简单的理解:Query是模型的输入,Key和Value是模型之间的状态,attention is all you need 论文中这个经典的图可以说明past_key_value是啥,但是,这是seq2seq或者生成模型才用到的结构,对于bert这个只有encoder的要用到这个吗? transformer中只要有注意力计算,就需...
1. 用如下脚本可以下载HuggingFace上的各种模型, 网址https://huggingface.co/models download.py #coding=gbkimporttimefromhuggingface_hubimportsnapshot_download#huggingface上的模型名称repo_id ="LinkSoul/Chinese-Llama-2-7b-4bit"#本地存储地址local_dir ="E:\\work\\AI\\GPT\\llama_model_7b_4bit"cache...
深入探讨Hugging Face的BertModel中的一些关键类和参数，整理如下：一、PreTrainedModel PreTrainedModel类位于transformers.modeling_utils文件中，提供基本的预训练模型框架。初始化可以通过from_pretrained(path)或直接创建实例实现。二、BertPreTrainedModel BertPreTrainedModel继承自PreTrainedModel，专门针对BERT模型...
In May, we announced a deepened partnership with Hugging Face and we continue to add more leading-edge Hugging Face models to the Azure AI model catalog on a...
01-ai/Yi-VL-34B · Hugging Face Yi-VL-34B模型托管在Hugging Face上,是全球首个开源的340亿视觉语言模型,代表了人工智能领域的重大进展。它以其双语多模态能力脱颖而出,可以进行英文和中文的多轮文本-图像对话。该模型在图像理解方面表现出色,并在MMMU和CMMMU等基准测试中... 内容导读 Yi-VL-34B模型托管在...
Hugging Faceis anopen source platform, also known as a “model hub” where you can find a collection of machine learning models and datasets. The platform provides the infrastructure to develop and train your machine learning models. This includes everything from writing the initial code to deplo...
### Building the Next Generation of Open-Source and Bilingual LLMs 🤗 Hugging Face • 🤖 ModelScope • ✡️ WiseModel 👋 Join us 💬 WeChat (Chinese) ! * * * 📕 Table of Contents * * * # What is Yi? ## Introduction * 🤖 The Yi series models are the next generat...
In the case of the discovered model, since the malicious payload is inserted at the beginning of the Pickle stream, execution of the model wouldn’t be detected as unsafe by Hugging Face’s existing security scanning tools. Protect your ML models RL is constantly improving its malware detection...
self.torchscript = kwargs.pop("torchscript", False) # Only used by PyTorch models self.use_bfloat16 = kwargs.pop("use_bfloat16", False) self.pruned_heads = kwargs.pop("pruned_heads", {}) # Is decoder is used in encoder-decoder models to differentiate encoder from decoder ...