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Open nowPosted 34 days ago

大模型训练与评测科研实习生

sii-czxy/campus170 open roles

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This job: posted 34 days ago

The posting

1. 阅读并整理大模型规模扩展规律（Scaling Law）、训练效率、数据配比优化以及模型评测方法等方向的前沿论文，学习并总结大模型实验设计、性能分析和规律探索的常用方法。 2. 参与大模型训练数据处理工作，根据实验需求完成数据筛选、清洗、采样和规模配置，构建不同数据规模、数据比例和训练设置下的实验数据方案。 3. 在指导下开展不同模型规模、训练数据规模以及训练策略条件下的实验，记录模型训练过程、计算资源消耗、训练效率和性能指标变化。 4. 参与模型评测流程设计，使用不同评测任务分析模型能力变化，整理实验结果，并通过可视化方法展示模型规模、数据量和计算投入对性能提升的影响规律。 5. 对实验数据进行基础统计分析，包括性能曲线绘制、Scaling Law 曲线拟合、不同实验设置对比分析等，探索模型规模扩展过程中的性能变化趋势。 6. 维护实验代码、配置文件、数据版本和结果文档，保证实验过程可复现，参与课题组讨论，并根据实验发现协助开展后续研究探索。

1. 招募对象：本科生。 2. 计算机、人工智能、数学、统计、电子信息等相关专业背景优先。 3. 具备基础的线性代数、概率统计和机器学习知识，对大模型训练、多模态模型和 Scaling Law 研究方向具有兴趣。 4. 具备 Python 编程基础，能够完成数据处理、实验脚本编写和结果可视化；了解 PyTorch 等深度学习框架者优先。 5. 能够阅读英文论文和技术资料，具备基本的信息整理、实验复现和结果分析能力。 6. 不要求已有正式科研经历，更关注学习能力、研究兴趣和实验执行能力；具有模型训练、论文复现、数据分析、数学建模或相关课程项目经历者优先。 7. 具备良好的实验记录习惯和数据分析意识，能够认真整理实验过程、分析实验结果，并根据实验现象提出问题。 8. 对大模型训练效率、模型能力扩展规律、多模态智能发展方向具有持续兴趣，能够稳定投入科研时间。 9. 实习安排：线下科研实习；实习周期：6个月以上；每周投入时间：20–30小时。

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