LLM data processing-Alpaca-CoT,Platform For AI:Machine Learning Designer of Platform for AI (PAI) provides various data processing components to help you edit, convert, filter, and deduplicate data. You can combine different components to filter h...
The circuitry is to: during processing of the constant weight values and key value entries associated with the first transformer kernel of the LLM neural network, pre-fetch constant weight values and key value entries associated with a second transformer kernel of the LLM neural network into a ...
4、反馈驱动的数据处理(FEEDBACK-DRIVEN DATA PROCESSING) Data-Juicer提供了可视化、自动评估等功能,形成了数据处理和LLM训练的闭环。它还引入了超参数优化,加速了数据处理的迭代。此外,Data-Juicer与LLM训练和评估生态系统无缝集成,支持自动评估。 4.1 HPO for Data Processing Data-Juicer 将超参数优化(HPO)概念应用...
Are there any special considerations for unstructured data processing? Ensure that the platform can: Support the full range of input and output connectors to data sources (e.g., Microsoft SharePoint, Atlassian Confluence), object stores and vector databases (e.g., Pinecone, Weaviate) Work with...
# for distributed processing executor_type: default # type of executor, support "default" or "ray" for now. ray_address: auto # the address of theRaycluster. # only for data analysis save_stats_in_one_file: false # whether to store all stats result into one file ...
Systematic & Reusable: Empowering users with a systematic library of 100+ coreOPs, and 50+ reusable config recipes and dedicated toolkits, designed to function independently of specific multimodal LLM datasets and processing pipelines. Supporting data analysis, cleaning, and synthesis in pre-training, ...
Coupled with multi-dimension automatic evaluation capabilities, it supports a timely feedback loop at multiple stages in the LLM development process. Comprehensive Data Processing Recipes: Offering tens of pre-built data processing recipes for pre-training, fine-tuning, en, zh, and more scenarios....
Build relevant capabilities (such as vector databases and data pre- and post-processing pipelines) into the existing data architecture, particularly in support of unstructured data. Focus on key points of the data life cycle to ensure high quality. Develop multiple interventions—both human and autom...
"LLM Prompt Engineering For Developers" begins by laying the groundwork with essential principles of natural language processing (NLP), setting the stage for more complex topics. It methodically guides readers through the initial steps of understanding how large language models work, providing a solid...
which plays a vital role in LLMs' performance. Existing open-source tools for LLM data processing are mostly tailored for specific data recipes. To continuously uncover the potential of LLMs, incorporate data from new sources, and improve LLMs' performance, we build a new system named Data-Ju...