<?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>Evaluation on TPOW Lab</title><link>https://tpow-001.netlify.app/tags/evaluation/</link><description>Recent content in Evaluation on TPOW Lab</description><generator>Hugo</generator><language>en</language><copyright>Copyright &amp;copy; 2025-2026 TPOW-001. All Rights Reserved.</copyright><lastBuildDate>Thu, 18 Jun 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://tpow-001.netlify.app/tags/evaluation/index.xml" rel="self" type="application/rss+xml"/><item><title>Strands Evals 完整教學</title><link>https://tpow-001.netlify.app/post/2026-06-18-evals-tutorial/</link><pubDate>Thu, 18 Jun 2026 00:00:00 +0000</pubDate><guid>https://tpow-001.netlify.app/post/2026-06-18-evals-tutorial/</guid><description><![CDATA[<blockquote>
<p><strong>Repository</strong>: <a href="https://github.com/strands-agents/evals" target="_blank" rel="noopener noreferrer">https://github.com/strands-agents/evals<i class="fas fa-external-link-square-alt ms-1"></i></a>
<strong>Stars</strong>: 144 | <strong>Forks</strong>: 39 | <strong>Language</strong>: Python | <strong>License</strong>: Apache-2.0
<strong>Tags</strong>: Evaluation, Testing, LLM, Agentic AI, Machine Learning
<strong>PyPI</strong>: <code>strands-agents-evals</code> | <strong>Python</strong>: 3.10+
<strong>Homepage</strong>: <a href="https://strandsagents.com" target="_blank" rel="noopener noreferrer">https://strandsagents.com<i class="fas fa-external-link-square-alt ms-1"></i></a></p></blockquote>
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<h2 id="1-專案概覽-project-overview" data-numberify>1. 專案概覽 (Project Overview)<a class="anchor ms-1" href="#1-專案概覽-project-overview"></a></h2>

<h3 id="11-這是什麼" data-numberify>1.1 這是什麼<a class="anchor ms-1" href="#11-這是什麼"></a></h3>
<p>Strands Evals 是 <strong>Strands Agents 生態系</strong> 中的綜合評估框架 (Comprehensive Evaluation Framework)，由 AWS 開源團隊開發維護。它為 AI Agent 與 LLM 應用提供從簡單的輸出驗證 (Output Validation) 到複雜的多 Agent 互動分析 (Multi-Agent Interaction Analysis)、軌跡評估 (Trajectory Evaluation)、自動化實驗生成 (Automated Experiment Generation) 等全方位評估能力。</p>]]></description></item><item><title>NVlabs/Nemotron-Labs-Diffusion 詳細教學 — 擴散 LM × Linear 自推測 × DGX Spark serving 完整實作指南</title><link>https://tpow-001.netlify.app/post/2026-06-02-nemotron-labs-diffusion-tutorial/</link><pubDate>Tue, 02 Jun 2026 00:00:00 +0000</pubDate><guid>https://tpow-001.netlify.app/post/2026-06-02-nemotron-labs-diffusion-tutorial/</guid><description><![CDATA[<h1 id="nvlabsnemotron-labs-diffusion--tri-mode-擴散語言模型完整教學" data-numberify>NVlabs/Nemotron-Labs-Diffusion — Tri-Mode 擴散語言模型完整教學<a class="anchor ms-1" href="#nvlabsnemotron-labs-diffusion--tri-mode-擴散語言模型完整教學"></a></h1>
<blockquote>
<p>本教學針對 <code>NVlabs/Nemotron-Labs-Diffusion</code>（commit <code>2026-05-28</code>，stars 32，dual license: Apache-2.0 code + NOML weights）撰寫，為內部知識庫的「擴散 LM 工程化 reference」。</p>]]></description></item></channel></rss>