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arXiv:2402.17834cs.CL

Stable LM 2 1.6B Technical Report

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.

arXiv comments
23 pages, 6 figures
Journal reference
DOI
Subjects
cs.CLComputation and Language stat.MLMachine Learning (Statistics)

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Linked by 2 identifiable commenters in 2 comments across 2 threads, from Feb 2024 to Mar 2024. Found in 1 HN submission from Feb 2024 to Feb 2024. Open submission discussions.

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    • We’ve published our technical report on Stable LM 2 1.6B; our state-of-the-art small language model trained on multilingual data. This in-depth study covers the training methods utilized for both the base and instruction-tuned versions of the model. Leading in the OpenLLM…

      BryanLegendView on HNParent context

    • StableLM 1.6B is a strong multilingual LM trained for 2T tokens of multilingual dataset. It offers strong performance compared to other models of the same size range including Google’s Gemma, Microsoft’s Phi2 and ByteDance’s Qwen. The technical report discloses all of the…

      omnipotent_iView on HNParent context

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