{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/stable-lm-2-1-6b-technical-report","title":"Stable LM 2 1.6B Technical Report","arxiv_id":"2402.17834","date":"2024-02-27","proceeding":null,"authors":["Marco Bellagente","Jonathan Tow","Dakota Mahan","Duy Phung","Maksym Zhuravinskyi","Reshinth Adithyan","James Baicoianu","Ben Brooks","Nathan Cooper","Ashish Datta","Meng Lee","Emad Mostaque","Michael Pieler","Nikhil Pinnaparju","Paulo Rocha","Harry Saini","Hannah Teufel","Niccolo Zanichelli","Carlos Riquelme"],"abstract":"We introduce StableLM 2 1.6B, the first in a new generation of our language model series. In this technical report, we present in detail the data and training procedure leading to the base and instruction-tuned versions of StableLM 2 1.6B. The weights for both models are available via Hugging Face for anyone to download and use. The report contains thorough evaluations of these models, including zero- and few-shot benchmarks, multilingual benchmarks, and the MT benchmark focusing on multi-turn dialogues. At the time of publishing this report, StableLM 2 1.6B was the state-of-the-art open model under 2B parameters by a significant margin. Given its appealing small size, we also provide throughput measurements on a number of edge devices. In addition, we open source several quantized checkpoints and provide their performance metrics compared to the original model.","url_abs":"https://arxiv.org/abs/2402.17834v1","url_pdf":"https://arxiv.org/pdf/2402.17834v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"stable-lm-2-1-6b-technical-report","repo_url":"https://github.com/yangyucheng000/University/tree/main/model-3/stablelm","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"}],"methods":[{"method_slug":"base","method_name":"BASE"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2402.17834","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}