BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

Explosive eruptions at mid-ocean ridges driven by CO2-rich magmas

Author Posting. © The Authors, 2011. This is the author's version of the work. It is posted here by permission of Nature Publishing Group for personal use, not for redistribution. The definitive version was published in Nature Geoscience 4 (2011): 260–263, doi:10.1038/ngeo1104.The abundance of volatile compounds, and particularly 18 CO2, in the upper oceanic mantle affects the style of volcanic eruptions. At mid-ocean ridges, eruptions are generally dominated by the gentle effusion of basaltic lavas with a low volatile content. But, explosive volcanism has been documented at some ocean spreading centres1-3, indicative of abundant volatile compounds. Estimates of the initial CO2 concentration of primary magmas can be used to constrain the CO2 content of the upper oceanic mantle, but these estimates vary greatly4,5. Here we present ion microprobe measurements of the CO2 content of basaltic melt trapped in plagioclase crystals. The crystals are derived from volcanic ash deposits erupted explosively at Axial Seamount, Juan de Fuca Ridge, in the northeast Pacific Ocean. We report unusually high CO2 concentrations of up to 9,160 ppm, which indicate that the upper oceanic mantle is more enriched in carbon than previously thought. And we furthermore suggest that CO2 fluxes along mid-ocean ridges4,5 vary significantly. Our results demonstrate that elevated fluxes of CO2 from the upper oceanic mantle can drive explosive eruptions at mid-ocean ridges.The expedition and DAC were supported through a grant to MBARI from the David and Lucile Packard Foundation. C.H. was supported by R.H. Tomlinson, GEOTOP and J.W. McConnell Memorial Fellowships at McGill University. J.S. was supported by grants from the Natural Sciences and Engineering Research Council of Canada

Highly oxidising conditions in volatile-rich El Hierro magmas: implications for ocean island magmatism

Abstract Recent studies investigating magmatic volatile contents indicate widespread enrichment of carbon, sulfur, and halogens in ocean island basalts (OIBs). At El Hierro in the Western Canary Islands, magmas with exceptionally high CO2 and S contents have been erupting throughout the Holocene. High S content of up to 5200 ppm requires an oxidised mantle source, but estimates of initial magmatic oxygen fugacity (fO2) are sparse. Here, we present estimates of fO2 and magmatic temperature for El Hierro together with a global mantle potential temperature dataset to evaluate redox and temperature conditions in the early stages of melt evolution for volatile-rich OIBs. Oxygen fugacities calculated using vanadium partitioning between melt inclusions (MIs) and their olivine hosts are &amp;gt;FMQ + 2.9 (2.9 log10 units above the fayalite-magnetite-quartz buffer), indicating that El Hierro magmas are highly oxidised. MI and matrix glass sulfur speciation data record fO2 between FMQ-1 to FMQ + 2; these values strongly depend on the position of the S2− to S6+ transition relative to the FMQ buffer. Nonetheless, glass sulfur speciation data record lower oxygen fugacity than V partitioning data, indicating MIs were able to maintain Fe3+/ΣFe and S6+/ΣS equilibrium with the surrounding melt during their evolution. The high fO2 of El Hierro magmas is coupled with an average mantle potential temperature estimate of 1443 ± 66°C (1σ, n = 17) for the broader Canary Islands, which is slightly higher than the average potential temperature estimated for adjacent mid-ocean ridge segments (1427 ± 33°C, 1σ, n = 474), albeit the two values are well within error. We find that ~98% of Canary Island rock compositions are not suitable for calculation of mantle potential temperatures using currently available methods. This is caused by the presence of substantial pyroxenite and volatile-enriched peridotite mantle domains under the Canary Islands. A wider compositional calibration of various petrological models is necessary to precisely determine mantle potential temperatures for volatile-rich alkali basalts. Our high oxygen fugacity estimates for El Hierro magmas reflect the fertile, fusible, and volatile-enriched nature of the mantle source beneath the Western Canary Islands.</jats:p

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License