关键词 "ENL Semantic Spinning" 的搜索结果, 共 24 条, 只显示前 480 条
YouTube MCP Server is an AI-powered solution designed to revolutionize your YouTube experience. It empowers users to search for YouTube videos, retrieve detailed transcripts, and perform semantic sear
🔍 This project implements a Model Context Protocol (MCP) server for interacting with the Semantic Scholar API. It provides tools for searching papers, retrieving paper and author details, and fetching
Mirror of
MCP server for semantic search with Qdrant vector database
Model Context Protocol (MCP) Server for EigenLayer
A long-term memory storage system for LLMs using the Model Context Protocol (MCP) standard. This system helps LLMs remember the context of work done over the entire history of a project, even across m
Model Control Protocol (MCP) server for ElevenLabs Scribe ASR API
MCP server to search up-to-date elasticsearch docs
An intelligent code memory system that leverages vector embeddings, structured databases, and knowledge graphs to store, retrieve, and analyze code patterns with semantic search capabilities, quality
MCP Server for querying DBT Semantic Layer
MCP Server for Interacting with Cube Semantic Layers
MCP server providing a knowledge graph implementation with semantic search capabilities powered by Qdrant vector database
MCP server for Semantic Scholar to search for papers
Model Context Protocol (MCP) server implementation for semantic vector search and memory management using TxtAI. This server provides a robust API for storing, retrieving, and managing text-based memo
A FastMCP server implementation for the Semantic Scholar API, providing comprehensive access to academic paper data, author information, and citation networks.
Knowledge management system that allows you to build a persistent semantic graph from conversations with AI assistants. All knowledge is stored in standard Markdown files on your computer, giving you
MCP server providing semantic memory and persistent storage capabilities for Claude using ChromaDB and sentence transformers.
IFAdapter是一种新型的文本到图像生成模型,由腾讯和新加坡国立大学共同推出。提升生成含有多个实例的图像时的位置和特征准确性。传统模型在处理多实例图像时常常面临定位和特征准确性的挑战,IFAdapter通过引入两个关键组件外观标记(Appearance Tokens)和实例语义图(Instance Semantic Map)解决问题。外观标记用于捕获描述中的详细特征信息,实例语义图则将特征与特
QwenLong-L1-32B 是阿里巴巴集团 Qwen-Doc 团队推出的,基于强化学习训练的首个长文本推理大模型。模型基于渐进式上下文扩展、课程引导的强化学习和难度感知的回顾性采样策略,显著提升在长文本场景下的推理能力。模型在多个长文本文档问答(DocQA)基准测试中表现优异,平均准确率达到了70.7%,超越OpenAI-o3-mini和Qwen3-235B-A22B等现有旗舰模型,且与Cla
只显示前20页数据,更多请搜索
Showing 121 to 144 of 144 results