<?xml version="1.1" encoding="utf-8"?>
<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">EIR</journal-id><journal-title-group><journal-title>Educational Innovation Research</journal-title></journal-title-group><issn>3029-1844</issn><eissn>3029-1852</eissn><publisher><publisher-name>WHIOCE PUBLISHING PTE. LTD.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.18063/EIR.v3i11.1702</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>Research Review on Practical Course Design for Localization Deployment of Open-source Large Language Models — Practical Path of Industry-Education Collaboration Based on Ollama/vLLM Toolchain</title><url>https://artdesignp.com/journal/EIR/3/11/10.18063/EIR.v3i11.1702</url><author>WangGenyuan,LiWenshuang</author><pub-date pub-type="publication-year"><year>2025</year></pub-date><volume>3</volume><issue>11</issue><history><date date-type="pub"><published-time>2025-12-20</published-time></date></history><abstract>&amp;nbsp;Addressing the widespread reliance on cloud-based Application Programming Interfaces (APIs) in AI courses at vocational colleges and students&amp;rsquo; limited practical experience in localized model deployment, this study systematically reviews the research status and implementation approaches of localized deployment of open-source large language models (LLMs)&amp;nbsp;in vocational AI training programs. Through bibliometric analysis, case study analysis, and theoretical framework construction, we synthesize domestic and international research findings since 2020, focusing on the technological evolution of open-source LLMs, existing challenges in vocational AI education, and critical elements of localized course design. The study reveals a significant structural imbalance in vocational AI education characterized by &amp;ldquo;technological maturity versus pedagogical lag&amp;rdquo;: while open-source models like LLaMA3 and Qwen2.5 can efficiently run on consumer-grade GPUs, and toolchains such as Ollama and vLLM have substantially reduced deployment barriers, over 90% of institutions still operate AI courses at the cloud API invocation level. Building on this, we propose a localized course design framework encompassing three dimensions: technology stack selection, curriculum module design, and industry-academia collaboration mechanisms. The Ollama platform integrated with the vLLM toolchain employs Low-Rank Adaptation (LoRA) lightweight fine-tuning technology, representing the most technically viable and pedagogically applicable localized AI training solution for vocational colleges. The school-enterprise &amp;ldquo;dual-track&amp;rdquo; curriculum collaboration mechanism forms the institutional foundation for ensuring high-quality implementation of such courses. The proposed &amp;ldquo;theory-instrument-practice&amp;rdquo; three-stage framework provides replicable reference models for peer institutions across the province.</abstract><keywords>Open-source large language model, Localized deployment, Vocational AI education, Industry-education collaborative training, LoRA fine-tuning, Ollama</keywords></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>[1] Wang H, Lu W, Wang C, 2025, Design and Implementation of Distributed Local Large Model Services Based on OpenWebUI and LiteLLM. Yangtze River Information and Communications, 38(12): 20&amp;ndash;24.
[2] Deng Y, 2025, Research on Information Security Risk Control Strategies for Localized Deployment of Large Models in Archival Management. Heilongjiang Archives, 2025(5): 141&amp;ndash;143.
[3] Jin H, Cui C, 2025, Research on Customized AI Large Models Based on Localization and Privatized Databases. Architectural Technology, 56(20): 2480&amp;ndash;2482.
[4] Song H, 2025, IT Operations Knowledge Base Based on Localized Deployment of Artificial Intelligence Models. Computer Programming Techniques and Maintenance, 2025(9): 122&amp;ndash;124 + 132.
[5] Li R, Yang J, 2025, Research on Localization Deployment and Security Protection System Based on DeepSeek Large Model. Wireless Interconnection Technology, 22(17): 1&amp;ndash;6.
[6] Tao X, 2024, Research on Large Language Model-Based Intelligent Q&amp;amp;A System Based on Hybrid Architecture. Post and Telecommunications Design Technology, 2024(5): 48&amp;ndash;55.</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
