Developer Uses Online LLM to Script Local 3B Model for Automated Renaming and Summarization of 400+ Conference Papers
English summary
Ian Cutress encountered a conference distribution of 400+ papers as numbered PDFs, making them unidentifiable. He used a large online AI model to generate an 800-line Python script that utilized Ollama to download and run a local Llama 3B model. The script looped through each PDF, had the local model propose a descriptive filename (including institution), renamed the file, and generated a 300-token summary. The process required about one hour online for scripting and three hours of local compute, saving weeks of manual effort. The approach demonstrates a hybrid AI workflow where a powerful cloud model handles complex code generation and a smaller local model efficiently performs bulk inference tasks.
Chinese summary
Ian Cutress 在一次会议中遇到 400 多篇仅以编号命名的 PDF 论文，难以查找。他使用在线大参数 AI 工具生成了一个约 800 行的 Python 脚本，该脚本通过 Ollama 下载并运行本地 Llama 3B 模型，批量读取每篇论文，让本地模型建议包含机构信息的文件名并重命名，同时生成 300 token 的摘要。整个过程在线构建脚本约 1 小时，本地运行 3 小时；若手动完成重命名和摘要需数周。这展示了混合 AI 工作流程：云端大模型负责复杂代码生成，而本地小模型高效完成批量推理任务。
Key points
Over 400 conference papers were distributed as session-numbered files, making them impossible to identify at a glance.
400 多篇会议论文以会话编号命名，难以快速识别。
A large online AI model generated an 800-line Python script to automate processing.
在线大模型生成了一个 800 行的 Python 脚本以自动化处理。
The script downloaded and used a local Llama 3B model via Ollama to rename files and produce 300-token summaries per paper.
脚本通过 Ollama 下载并使用本地 Llama 3B 模型，为每篇论文重命名并生成 300 token 的摘要。
The automation took 1 hour of online time for scripting and 3 hours of local compute, versus weeks of manual work.
自动化花费 1 小时在线编写脚本和 3 小时本地运行，而手动工作需要数周。
The workflow demonstrates using a powerful cloud model for complex code generation and a smaller local model for bulk inference tasks.
工作流程展示了用强大的云模型生成复杂代码，再用本地小模型进行批量推理的混合模式。