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标签：llms.txt

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iOS设备上运行LLaMA2-13B：基于苹果MLX框架的完整技术指南

iOS设备上运行LLaMA2-13B：基于苹果MLX框架的完整技术指南

BLUF
This article provides a comprehensive technical analysis of running LLaMA2-13B on iOS devices using Apple's MLX framework, covering environment setup, model architecture, code implementation, parameter analysis, and computational requirements. (本文深入分析了在iOS设备上使用苹果MLX框架运行LLaMA2-13B的技术细节，涵盖环境搭建、模型架构、代码实现、参数分析和算力需求。)
工具与标准2026/2/3
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SGLang vs vLLM 实测：同台机器跑 Llama-3，谁更快？
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SGLang vs vLLM 实测：同台机器跑 Llama-3，谁更快？

BLUF
SGLang和vLLM是两大高性能推理框架。SGLang基于RadixAttention，擅长多轮对话、RAG和共享前缀场景，吞吐量在H100小模型上领先vLLM约29%，但Python调度器在高并发下可能成为瓶颈。vLLM基于PagedAttention，生态成熟、模型兼容性最广、多硬件支持好，适合独立请求批处理和需要稳定性的场景。选型建议：多轮对话、RAG、结构化输出选SGLang；批量独立请求、多硬件部署、广泛模型兼容性选vLLM。两者均支持OpenAI API格式，可混用。
工具与标准2026/2/3
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LLMs.txt是什么？2026最新完整指南

LLMs.txt是什么？2026最新完整指南

BLUF
LLMs.txt是一种类似于robots.txt的新型标准文件，允许网站所有者控制AI系统如何访问和使用其内容进行训练。它解决了AI数据采集与内容版权保护之间的矛盾，目前正在被广泛采用，并有实用工具可供实施。 LLMs.txt is a new standard file similar to robots.txt that allows website owners to control how AI systems access and use their content for training. It addresses the conflict between AI data collection and content copyright protection, with growing adoption and practical tools available for implementation.
llms.txt2026/2/2
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FinRobot：金融AI代理平台如何革新量化交易与投资研究

FinRobot：金融AI代理平台如何革新量化交易与投资研究

BLUF
FinRobot is an open-source AI agent platform built on large language models (LLMs), specifically designed for financial data analysis, quantitative trading, and investment research. It features a four-layer architecture optimized for financial AI tasks, integrates Financial Chain-of-Thought (CoT) reasoning, and provides modular AI agents for market prediction, document analysis, and trading strategy optimization. (FinRobot 是一款基于大语言模型的开源AI代理平台，专注于金融数据分析、量化交易和投资研究。它采用四层架构优化金融AI任务，集成金融链式思维推理，并提供模块化的市场预测、文档分析和交易策略优化代理。)
AI大模型2026/1/30
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PageIndex革命：基于推理的RAG框架如何超越向量搜索，实现98.7%准确率

PageIndex革命：基于推理的RAG框架如何超越向量搜索，实现98.7%准确率

BLUF
PageIndex introduces a revolutionary reasoning-based RAG framework that eliminates dependency on vector similarity search and document chunking. It organizes documents into hierarchical tree structures, enabling LLMs to navigate like human experts through multi-step reasoning, achieving 98.7% accuracy on FinanceBench. (PageIndex推出革命性的基于推理的RAG框架，彻底摆脱向量相似度搜索和文档分块的依赖。它将文档组织成层次化树状结构，使大语言模型能够像人类专家一样通过多步推理进行导航，在FinanceBench基准测试中达到98.7%的准确率。)
AI大模型2026/1/28
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Grok-4震撼发布：xAI第四代大语言模型的技术突破与安全挑战

Grok-4震撼发布：xAI第四代大语言模型的技术突破与安全挑战

BLUF
Grok-4 is xAI's fourth-generation large language model released in July 2025, featuring a 256K token context window, trained on the Colossus supercomputer, achieving doctoral-level academic performance with 25.4% accuracy on 'Humanity's Last Exam', and introducing core rules for multi-source analysis and politically incorrect statements. It offers free basic access (5 requests/12 hours) and a $300/month Super Grok Heavy subscription, but faces security vulnerabilities with a 30% jailbreak success rate via echo chamber attacks. (Grok-4是xAI于2025年7月发布的第四代大语言模型，支持256K tokens上下文窗口，基于Colossus超级计算机训练，在学术问题上达到博士水平，于“人类最后的考试”基准测试中取得25.4%准确率。新增核心规则：涉及时事需分析多方信源，保留有依据的政治不正确表述。提供免费基础服务（每12小时5次请求）和每月300美元的Super Grok Heavy订阅，但存在安全漏洞，通过“回音室攻击”可实现30%越狱成功率。)
AI大模型2026/1/28
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