10.3 第三步:获得 text embedding:CLS pooling 10.4 第四步:使用FAISS构建索引 10.5 第五步:调用 FAISS 进行模糊搜索 学习的要义:1. 系统性学习:只言片语的学,只会一叶遮目,只见树木,不见森林。系统性的学习,才能知道事物的始末,你才能知道怎么创造。2. 多实战:学习最好的方式是教给别人,因为讲,需要条理清...
Code Issues Pull requests Implement RAG using LangChain and HuggingFace embedding models question-answering rag fastapi streamlit langchain huggingface-embeddings Updated Sep 14, 2024 Jupyter Notebook rooneyrulz / memomind-ai-chatbot Star 2 Code Issues Pull requests Memomind is a sleek note-ta...
Fix: Change embeddings to embedding by @mokeddembillel in #2738 fix response type of document for Text Generation Inference by @jitokim in #2743 Upgrade outlines to 0.1.1 by @aW3st in #2742 Upgrading our deps. by @Narsil in #2750 feat: return streaming errors as an event formatted for...
AddHuggingFaceImageToText(IServiceCollection, String, Uri, String, String, HttpClient) Adds an Hugging Face image-to-text service with the specified configuration. AddHuggingFaceTextEmbeddingGeneration(IServiceCollection, String, Uri, String, String, HttpClient) Adds an Hugging Face text embedding gener...
Embedding Space Alignment (嵌入空间对齐) Visual Instructions Injection (视觉指令注入) 打分 实用性:4.5分 创新性:4.8分 推荐度:4.7分 到Huggingface 论文主页查看详情 InstructDiffusion: A Generalist Modeling Interface for Vision Tasks 本文主要工作:
每天10分钟,带您快速了解当日HuggingFace热门AI论文内容。每个工作日更新,欢迎订阅。 📢播客节目在小宇宙、Apple Podcast平台搜索【HuggingFace 每日AI论文速递】 🖼另外还有图文版,可在小红书搜索并关注【AI速递】 Technology Updated weekly Investing Investing ...
Provides extension methods for theIServiceCollectioninterface to configure Hugging Face connectors. C# publicstaticclassHuggingFaceServiceCollectionExtensions Inheritance Object HuggingFaceServiceCollectionExtensions Methods 展開表格 Applies to 產品版本 Semantic Kernel DotnetLatest ...
FixTextGenerationPipeline.__call__docstring by @alvarobartt in #29491 Substantially reduce memory usage in _update_causal_mask for large batches by using .expand instead of .repeat [needs tests+sanity check] by @nqgl in #29413 Fix: Disable torch.autocast in RotaryEmbedding of Gemma and LLaM...
self.wte = nn.Embedding(self.vocab_size, self.embed_dim) self.drop = nn.Dropout(config.emb_dropout_prob) if config.rotary_pct == 1.0: self.rotary_ndims = None else: assert config.rotary_pct < 1 self.rotary_ndims = int( ...
class Embedding(torch.nn.Module): """Language model embeddings.""" def __init__(self, config: ChatGLMConfig, device=None): super(Embedding, self).__init__() self.hidden_size = config.hidden_size # Word embeddings (parallel)....