AI 中文总结
OpenLanguageModel是开源PyTorch库，用于构建和预训练小语言模型。其模型代码易读，能连接多种组件。通过追踪GPT - 2展示完整路径，含27个预设和文档。验证有高一致性和效率等成果，采用MIT许可，可多途径获取。
AI 中文摘要
开放语言模型（OLM）是一个用于构建和预训练小语言模型的开源PyTorch库，能保持模型机制可见。在OLM中，模型代码如同架构一样易读，组件为普通模块，特定模块描述连接方式。模型可从教学笔记本无缝过渡到完整预训练或研究消融。OLM连接可读模型层与分词器、数据集等。通过追踪GPT - 2展示完整路径，还包括27个预设和文档。验证表明与独立参考实现高度一致，有高弱缩放效率等成果。OLM采用MIT许可，可通过多种途径获取。
英文摘要
OpenLanguageModel (OLM) is an open-source PyTorch library for building and pretraining small language models while keeping their machinery visible. In OLM, model code reads like the architecture: components are ordinary modules, while Block, Residual, Repeat, and Parallel describe how they are wired. The resulting model can move unchanged from a teaching notebook to a complete pretraining run or a research ablation. OLM connects this readable model layer to tokenizers, local and streaming datasets, optimization, mixed precision, callbacks, checkpoints, and hardware-aware CPU, single-GPU, and single-node multi-GPU execution. We demonstrate the full path by tracing GPT-2 from diagram to code, launching a FineWeb-Edu training script, replacing one attention component, and letting AutoTrainer configure the available machine. The package includes 27 presets across nine familiar model families and documentation that progresses from LM fundamentals to architecture research. Validation shows close agreement with independent reference implementations, 90.6% four-GPU weak-scaling efficiency for a 348M-parameter workload, compact architecture edits, and positive early usability results. OLM is MIT-licensed and available through PyPI, GitHub, and its documentation site.
Comments8 pages, 3 figures, and 1 table. Website: https://openlanguagemodel.github.io/openlanguagemodel/. Code: https://github.com/openlanguagemodel/openlanguagemodel
