\n

Welcome to ServiceNow's page on HuggingFace!

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ServiceNow® is the AI platform for business transformation. We bring\nintelligence to every corner of your business by offering a single,\ncloud-based platform that combines AI, data, and workflows to help\nenterprises automate and manage critical processes across IT, HR,\nsecurity, and more. For more information on our company and its\nproducts, visit our corporate website: ServiceNow - Put AI to\nWork.

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On this site here, you will find open-source publications, including work from our\nfundamental AI research team.\nYou can also find more open-source publications on our GitHub\norganization.

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Discover below a few of the projects we're especially proud to showcase.

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Benchmarks

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BigDocsBench\nis a benchmark designed to evaluate VLM document understanding at scale.

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BrowserGym\nLeaderboard\nwas created to evaluate LLMs, VLMs, and agents on web navigation tasks.

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UI-Vision, a\nbenchmark for GUI visual grounding.

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Models

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BigCode is an open scientific\ncollaboration focused on the responsible development of LLM for code. It\naddresses the lack of transparency in LLM development by promoting open\ngovernance, open datasets, and collaborative research.

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StarCoder is a\nstate-of-the-art, 15 B-parameter open-source language model for code,\ntrained on 1 trillion tokens extracted from GitHub repositories spanning\nover 80 programming languages, and it achieves top performance on\nbenchmarks like HumanEval---surpassing both open and closed-source\nalternatives---while offering an extensive 8K+ context window and\nenhanced safety features like PII redaction and attribution tracing.

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Apriel-Nemotron-15b-Thinker,\na 15B-parameter reasoning model in ServiceNow's Apriel SLM series,\ndelivering state-of-the-art performance on both enterprise and academic\nbenchmarks while using only half the memory of larger models.

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StarVector, a\ncode-driven image generation framework.

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AlignVLM, a VLM that adapts\nvisual features for large language models

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Datasets

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The Stack v2 is\nthe largest open-access pretraining dataset for code-focused\nLLMs---featuring 67.5 TB (≈900 billion tokens) of meticulously curated,\ndeduplicated, and cleaned source code---enabling next-gen models like\nStarCoder2 to train effectively at scale.

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Repliqa is a\nhuman-curated evaluation dataset designed to test how well LLMs use\ncontextual information from provided documents. It contains\ncontext--question--answer triplets based on realistic but fictional\ndocuments about invented people, places, and events---removing the\nchance for models to rely on memorized facts.

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