<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>微调 on 硅基 AGI · 智能体学习与测评</title><link>https://guijiagi.com/tags/%E5%BE%AE%E8%B0%83/</link><description>Recent content in 微调 on 硅基 AGI · 智能体学习与测评</description><generator>Hugo</generator><language>zh-cn</language><copyright>本站内容采用 CC BY-NC-SA 4.0 国际许可协议授权</copyright><lastBuildDate>Thu, 16 Jul 2026 11:04:00 +0800</lastBuildDate><atom:link href="https://guijiagi.com/tags/%E5%BE%AE%E8%B0%83/index.xml" rel="self" type="application/rss+xml"/><item><title>大模型微调实战：LoRA、QLoRA与全参微调的选择策略</title><link>https://guijiagi.com/posts/b1-ca24c427/</link><pubDate>Thu, 16 Jul 2026 11:04:00 +0800</pubDate><guid>https://guijiagi.com/posts/b1-ca24c427/</guid><description>对比分析LoRA、QLoRA和全参微调的适用场景、技术细节与工程实践指南</description></item><item><title>LoRA微调实战指南：参数高效微调的原理、实践与陷阱</title><link>https://guijiagi.com/posts/b2-dae4b598/</link><pubDate>Thu, 16 Jul 2026 10:03:00 +0800</pubDate><guid>https://guijiagi.com/posts/b2-dae4b598/</guid><description>从LoRA数学原理到工程实现，覆盖秩选择、学习率配置、QLoRA及常见微调陷阱的完整指南</description></item><item><title>大模型微调中的灾难性遗忘问题</title><link>https://guijiagi.com/posts/article-67/</link><pubDate>Mon, 13 Jul 2026 04:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/article-67/</guid><description>深入分析大模型微调中灾难性遗忘的成因、影响和缓解策略，涵盖正则化、回放、参数隔离等方法</description></item><item><title>大模型微调的数据工程全流程</title><link>https://guijiagi.com/posts/article-36/</link><pubDate>Sun, 12 Jul 2026 22:50:00 +0800</pubDate><guid>https://guijiagi.com/posts/article-36/</guid><description>从数据采集到质量评估，大模型微调数据工程的完整实践指南</description></item><item><title>大模型微调的数据工程全流程</title><link>https://guijiagi.com/posts/b2-e781991d/</link><pubDate>Sun, 12 Jul 2026 22:50:00 +0800</pubDate><guid>https://guijiagi.com/posts/b2-e781991d/</guid><description>从数据采集到质量评估，大模型微调数据工程的完整实践指南</description></item><item><title>Hermes 4微调实战：从数据准备到模型部署全流程</title><link>https://guijiagi.com/posts/hermes4-finetuning-guide/</link><pubDate>Wed, 08 Jul 2026 13:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/hermes4-finetuning-guide/</guid><description>Nous Hermes 4微调完整指南：数据准备、LoRA训练、评估调优、量化部署的企业级实践</description></item><item><title>Nous Hermes 4架构解析：开源函数调用模型的新标杆</title><link>https://guijiagi.com/posts/nous-hermes4-architecture/</link><pubDate>Wed, 08 Jul 2026 11:30:00 +0800</pubDate><guid>https://guijiagi.com/posts/nous-hermes4-architecture/</guid><description>Nous Hermes 4架构深度解析：函数调用能力、微调方案、与Llama对比及企业应用实践</description></item><item><title>RAG还是微调：决策框架</title><link>https://guijiagi.com/posts/rag-vs-fine-tuning-decision/</link><pubDate>Thu, 02 Jul 2026 11:31:00 +0800</pubDate><guid>https://guijiagi.com/posts/rag-vs-fine-tuning-decision/</guid><description>何时用RAG、何时用微调？基于多维度评估的系统化决策框架</description></item><item><title>LoRA微调手把手教程</title><link>https://guijiagi.com/posts/lora-fine-tuning-step-by-step/</link><pubDate>Thu, 02 Jul 2026 11:30:00 +0800</pubDate><guid>https://guijiagi.com/posts/lora-fine-tuning-step-by-step/</guid><description>从环境搭建到模型部署，LoRA微调的完整手把手教程</description></item><item><title>微调数据准备最佳实践</title><link>https://guijiagi.com/posts/fine-tuning-data-preparation/</link><pubDate>Thu, 02 Jul 2026 11:29:00 +0800</pubDate><guid>https://guijiagi.com/posts/fine-tuning-data-preparation/</guid><description>从数据采集到质量控制，LLM微调数据准备的完整最佳实践</description></item><item><title>强化学习对齐 2026：从 RLHF 到 DPO 再到 ORPO</title><link>https://guijiagi.com/posts/alignment-evolution-rlhf-to-orpo/</link><pubDate>Tue, 30 Jun 2026 17:10:00 +0800</pubDate><guid>https://guijiagi.com/posts/alignment-evolution-rlhf-to-orpo/</guid><description>大模型对齐技术演进：RLHF、DPO、ORPO、GRPO等核心算法原理、实现要点与2026年最新进展</description></item><item><title>LoRA微调2026：从数据准备到部署的全流程</title><link>https://guijiagi.com/posts/lora-finetuning-2026-guide/</link><pubDate>Tue, 30 Jun 2026 09:40:00 +0800</pubDate><guid>https://guijiagi.com/posts/lora-finetuning-2026-guide/</guid><description>2026年LoRA微调的最新实践指南，涵盖数据工程、训练配置、评估优化到生产部署的完整流程</description></item><item><title>DPO 训练实践：偏好对齐的数据工程</title><link>https://guijiagi.com/posts/dpo-training-practice-preference-alignment-data-engineering/</link><pubDate>Sun, 28 Jun 2026 10:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/dpo-training-practice-preference-alignment-data-engineering/</guid><description>深入解析 DPO（Direct Preference Optimization）训练流程，涵盖偏好数据构建、训练配置和效果评估</description></item><item><title>LoRA 微调实战 2026：从数据准备到部署的完整流程</title><link>https://guijiagi.com/posts/lora-finetuning-2026-data-to-deployment/</link><pubDate>Sun, 28 Jun 2026 10:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/lora-finetuning-2026-data-to-deployment/</guid><description>手把手教你用 LoRA 微调大语言模型，涵盖数据工程、训练配置、评估和部署的全流程</description></item><item><title>SFT 数据质量评估：Bad Data 如何毁掉你的微调</title><link>https://guijiagi.com/posts/sft-data-quality-assessment-bad-data-destroys-finetuning/</link><pubDate>Sun, 28 Jun 2026 10:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/sft-data-quality-assessment-bad-data-destroys-finetuning/</guid><description>系统分析 SFT 数据质量问题对微调效果的影响，提供数据清洗、质量评估和持续监控的工程方案</description></item><item><title>大模型微调工具链 2026：LLaMA-Factory vs Axolotl vs Unsloth</title><link>https://guijiagi.com/posts/finetuning-toolchain-2026-llamafactory-axolotl-unsloth/</link><pubDate>Sun, 28 Jun 2026 10:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/finetuning-toolchain-2026-llamafactory-axolotl-unsloth/</guid><description>三大微调工具链全面对比：LLaMA-Factory、Axolotl、Unsloth的功能、性能与易用性评测</description></item><item><title>大模型蒸馏技术 2026：从 GPT-5.5 到 7B 模型的能力迁移</title><link>https://guijiagi.com/posts/llm-distillation-2026-gpt5-to-7b-capability-transfer/</link><pubDate>Sun, 28 Jun 2026 10:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/llm-distillation-2026-gpt5-to-7b-capability-transfer/</guid><description>系统介绍大模型知识蒸馏的最新技术，涵盖响应蒸馏、特征蒸馏、Agent 蒸馏和实际部署案例</description></item><item><title>Hermes微调实战指南</title><link>https://guijiagi.com/posts/hermes-finetune-guide/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/hermes-finetune-guide/</guid><description>Hermes微调实战指南</description></item><item><title>LoRA微调参数调优指南</title><link>https://guijiagi.com/posts/lora-finetune-tuning/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/lora-finetune-tuning/</guid><description>LoRA微调参数调优指南</description></item><item><title>LoRA微调参数调优指南</title><link>https://guijiagi.com/posts/lora-finetuning-tuning-guide/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/lora-finetuning-tuning-guide/</guid><description>LoRA微调的核心参数调优指南，从rank选择到学习率设置的全面实践</description></item><item><title>QLoRA量化微调指南</title><link>https://guijiagi.com/posts/qlora-finetune-guide/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/qlora-finetune-guide/</guid><description>QLoRA量化微调指南</description></item><item><title>大模型微调数据准备全流程</title><link>https://guijiagi.com/posts/finetune-data-preparation/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/finetune-data-preparation/</guid><description>大模型微调数据准备全流程</description></item><item><title>领域适配微调方案</title><link>https://guijiagi.com/posts/domain-adaptation-finetune/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/domain-adaptation-finetune/</guid><description>领域适配微调方案</description></item><item><title>领域适配微调方案</title><link>https://guijiagi.com/posts/domain-adaptation-finetuning/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/domain-adaptation-finetuning/</guid><description>从通用大模型到领域专家的微调方案，数据构建、训练策略与效果评估全流程</description></item><item><title>嵌入模型微调实战</title><link>https://guijiagi.com/posts/embedding-finetune-practice/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/embedding-finetune-practice/</guid><description>嵌入模型微调实战</description></item><item><title>嵌入模型微调实战</title><link>https://guijiagi.com/posts/embedding-model-finetuning/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/embedding-model-finetuning/</guid><description>嵌入模型领域微调的完整流程，从数据准备到训练评估的实战指南</description></item><item><title>RAG vs 微调：2026 年的场景选择指南</title><link>https://guijiagi.com/posts/rag-vs-fine-tuning-2026/</link><pubDate>Fri, 26 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/rag-vs-fine-tuning-2026/</guid><description>RAG 和微调不是非此即彼的选择，而是互补的知识注入策略。本文从 2026 年的实践视角，给出全场景决策框架。</description></item><item><title>QLoRA 微调实战：4bit 量化下的高效训练</title><link>https://guijiagi.com/posts/fine-tuning-qlora-guide/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/fine-tuning-qlora-guide/</guid><description>深入解析 QLoRA 的 NF4 量化、双重量化、分页优化器原理，提供完整的 LLaMA-3 QLoRA 微调代码实战，涵盖数据准备、训练配置、显存优化技巧与常见问题排查。</description></item><item><title>微调 vs RAG：什么场景该选什么方案</title><link>https://guijiagi.com/posts/fine-tuning-vs-rag-decision/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/fine-tuning-vs-rag-decision/</guid><description>从知识更新频率、推理深度、成本预算、数据隐私等维度，系统对比微调与 RAG 的适用场景，给出决策框架和混合方案。</description></item><item><title>LLM 微调流水线设计：从数据到部署的 MLOps</title><link>https://guijiagi.com/posts/llm-finetune-pipeline/</link><pubDate>Thu, 25 Jun 2026 10:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/llm-finetune-pipeline/</guid><description>完整的 LLM 微调 MLOps 流水线：数据准备、训练配置、评估、版本管理、A/B 测试与灰度发布</description></item><item><title>LoRA/QLoRA 微调实战指南：显存省 10 倍</title><link>https://guijiagi.com/posts/lora-qlora-finetune-guide/</link><pubDate>Thu, 25 Jun 2026 10:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/lora-qlora-finetune-guide/</guid><description>从原理到实战，全面解析 LoRA 和 QLoRA 参数高效微调技术，包含 Rank 选择、模块配置和 Unsloth 加速方案</description></item><item><title>RAG vs 微调：什么场景该用什么</title><link>https://guijiagi.com/posts/rag-vs-finetune-decision/</link><pubDate>Thu, 25 Jun 2026 10:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/rag-vs-finetune-decision/</guid><description>深入对比 RAG 与微调的技术特征、成本结构和适用场景，提供决策矩阵和混合方案建议</description></item><item><title>微调 vs Prompt 工程：何时该选哪个？</title><link>https://guijiagi.com/posts/fine-tuning-vs-prompt/</link><pubDate>Wed, 24 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/fine-tuning-vs-prompt/</guid><description>系统对比 Prompt 工程与微调的适用场景、成本、效果，提供决策矩阵和混合方案</description></item></channel></rss>