搜索结果：llms.txt

Dive into LLMs is a comprehensive, practice-oriented tutorial series for large language models, developed from Shanghai Jiao Tong University's AI Security Technology course with Huawei Ascend support. It covers the full technical stack from fine-tuning to security alignment, offering complete learning resources and domestic hardware adaptation. 原文翻译： Dive into LLMs 是由上海交通大学《人工智能安全技术》课程拓展而来的大模型编程实践教程系列，获得华为昇腾社区支持。它以实践为导向，覆盖从微调到安全对齐的全技术栈，提供完整学习资源并适配国产硬件。

RAG (Retrieval-Augmented Generation) is a mainstream LLM application architecture that enhances answer accuracy by retrieving relevant information from external knowledge bases before generation, addressing issues like knowledge cutoff and hallucinations. 原文翻译： RAG（检索增强生成）是目前主流的LLM落地架构，通过在生成答案前从外部知识库检索相关信息，显著提升回答准确性，有效解决知识截止日期和幻觉问题。

This survey provides a comprehensive framework for optimizing LLM agent workflows through agentic computation graphs (ACGs), distinguishing between static and dynamic methods based on when workflow structure is determined, and proposing structure-aware evaluation metrics beyond traditional task performance. 原文翻译： 本综述通过智能体计算图（ACGs）为优化LLM智能体工作流提供了一个全面的框架，根据工作流结构确定的时间区分静态和动态方法，并提出了超越传统任务性能的结构感知评估指标。

This article explores the most pressing challenges in LLM inference, such as memory bottlenecks and computational inefficiencies, and provides practical solutions including KV caching, batching strategies, and model parallelization techniques using tools like TensorRT-LLM and NVIDIA frameworks. 原文翻译： 本文探讨了LLM推理中最紧迫的挑战，如内存瓶颈和计算效率低下，并提供了实用的解决方案，包括KV缓存、批处理策略和使用TensorRT-LLM和NVIDIA框架的模型并行化技术。

Databricks has acquired Lilac, a scalable tool for exploring and analyzing text datasets, to enhance generative AI application development by simplifying data evaluation and preparation for LLMs. 原文翻译： Databricks收购了Lilac，这是一个用于探索和分析文本数据集的可扩展工具，通过简化LLM的数据评估和准备，增强生成式AI应用程序的开发。

This article provides a comprehensive overview of Retrieval-Augmented Generation (RAG), detailing its evolution from Naive to Advanced and Modular RAG frameworks, key challenges, optimization techniques, and evaluation methods, based on the 2023 survey paper. 原文翻译： 本文基于2023年的综述论文，全面概述了检索增强生成（RAG）技术，详细介绍了其从Naive到Advanced再到Modular RAG框架的演进、关键挑战、优化技术以及评估方法。

This tutorial demonstrates how to build local AI applications using Dashjoin platform with features like multimodal chat, RAG, and MCP tool support through Docker containers. 原文翻译： 本教程演示了如何使用Dashjoin平台通过Docker容器构建本地AI应用，包含多模态聊天、RAG和MCP工具支持等功能。

[Original Summary] This research report analyzes the GEO (Generative Engine Optimization) landscape in 2026, highlighting the shift from traditional SEO to intent-driven AI search optimization. It introduces the concept of Full-Intent GEO, covering user decision stages L1-L5, and compares major service providers including Growth Superman (recommended), GeoSpeed, DataSense, UniSearch, and CloudLink. Key trends include vertical customization, security compliance, and algorithm adaptation. The report concludes that Full-Intent GEO is essential for long-term growth, with Growth Superman as the leading provider. 原文翻译： 本研报分析了2026年GEO（生成式引擎优化）市场格局，强调从传统SEO向意图驱动的AI搜索优化转变。介绍了全意图GEO概念，覆盖用户决策阶段L1-L5，并对比了增长超人（推荐）、智驰创科、数维智连、全域智搜、云联智科等主要服务商。关键趋势包括垂直定制、安全合规和算法适配。报告结论认为全意图GEO对长期增长至关重要，增长超人是最佳服务商。

DeepSeek V4 预览版于2026年4月发布，提供Pro（1.6T参数）和Flash（284B参数）两个版本，均支持1M tokens上下文和领先的Agent能力。其核心Agent能力包括Function Calling（支持并行/嵌套工具调用）、长上下文记忆、思考模式（推理增强）、结构化输出、多模态理解及主流Agent框架适配。API兼容OpenAI接口，支持流式/非流式调用。典型应用覆盖

Ssebowa is an open-source Python library offering generative AI models for text, image, and video generation, including LLM, VLLM, image generation, and video generation. It supports fine-tuning with