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

Initial conditions for the marketing of flexibility in demand: status quo analysis and meta-study

Die vorliegende Arbeit ist Teil des durch das Bundesministerium für Bildung und Forschung geförderten Forschungsprojektes Synchronisierte und energieadaptive Produktionstechnik zur flexiblen Ausrichtung von Industrieprozessen auf eine fluktuierende Energieversorgung (SynErgie). Ziel des Forschungsprojektes ist die Befähigung der energieintensiven Industrien in Deutschland, die Stromnachfrage dem zunehmend fluktuierenden Stromangebot anzupassen. In der Vergangenheit waren Stromsysteme in der Regel dahingehend ausgelegt, dass die Erzeugungsseite des Marktes an das zeitliche Verhalten des Verbrauchs angepasst war. Durch den verstärkten Ausbau volatiler erneuerbarer Energien unterliegt die Stromerzeugung jedoch unkontrollierbaren, wetterabhängigen Schwankungen, weshalb eine Flexibilisierung des Gesamtsystems zunehmend an Bedeutung gewinnt. Die in SynErgie betrachteten Industrieprozesse stellen dabei eine Teilmenge potenzieller Flexibilisierungsoptionen dar und können zur Lastanpassung an schwankende Erzeugung sowie zur Bereitstellung von Systemdienstleistungen und Entlastung der Netze beitragen. In einem liberalisierten, wettbewerblichen Strommarkt sind im Hinblick auf die Erschließung der Potenziale der Nachfrageflexibilität die marktlichen und regulatorischen Rahmenbedingungen von hoher Relevanz. Diese Studie beschreibt daher zunächst die Grundlagen des Strommarktdesigns und des konstituierenden gesetzlichen Rahmens. Dabei wird stets der Bezug zur Anwendung auf Industrieprozesse genommen und potenzielle Hemmnisse der Partizipation flexibler Nachfrageprozesse aufgearbeitet. Die Analyse bildet den Ausgangspunkt für die folgenden Arbeitspakete im Cluster IV und dient der clusterübergreifenden Information über den Status Quo der Marktstrukturen und regulatorischen Rahmenbedingungen. Neben der systematischen Aufarbeitung des marktlichen Rahmens werden die wissenschaftliche Literatur sowie bereits publizierte Studien zum Thema Nachfrageflexibilität (Demand Side Management und Demand Response) in einer Metastudie analysiert und zusammengefasst

Initial conditions for the marketing of demand flexibility: status quo analysis and meta study. 2 version

The present work is part of the research project "Synchronized and energy-adaptive production technology for the flexible alignment of industrial processes to a fluctuating energy supply (SynErgie)" funded by the Federal Ministry of Education and Research. As one of the "Kopernikus projects for the energy transition", the SynErgie research project aims to enable energy-intensive industries in Germany to adapt their electricity demand to the increasingly fluctuating electricity supply. In the past, electricity systems were usually designed so that the generation side of the market was adapted to the temporal behavior of consumption. However, due to the increased expansion of volatile renewable energies, power generation is subject to uncontrollable, weather-dependent fluctuations, which is why making the overall system more flexible is becoming increasingly important. Because the producer side can only offer the required flexibility in the form of a reduction in feed-in, there is a so-called flexibility gap. As became clear on December 14, 2018 and January 10, 2019, this flexibility gap is already pushing the power system to its limits of stability. Only through the use of many compensation mechanisms or With options for flexibility, the security of supply could just be maintained on these days. The industrial processes considered in SynErgie represent a subset of potential flexibility options and can contribute to load adjustment to fluctuating generation as well as to the provision of system services and relief of the grids. In a liberalized, competitive electricity market, the market and regulatory framework conditions are of great relevance with regard to the development of the potential for flexibility in demand. This study therefore first describes the basics of the electricity market design and the constituent legal framework. Current discussions about the basic price system (unit price system vs. zonal system vs. nodal system) are not dealt with. The processing of these discussions as well as the specific analysis of the effects of the price system on demand flexibility is the content of the work packages of the Cluster IV “Market and Electricity System” pending in SynErgie II. The present study therefore rather works on potential obstacles to the participation of flexible demand processes and always refers to the application to industrial processes. The analysis forms the basis for future work in Cluster IV and provides cross-cluster information about the status quo of market structures and regulatory framework conditions. In addition to the systematic processing of the market framework, the scientific literature and already published studies on the subject of demand flexibility (demand side management and demand response) are analyzed and summarized in a meta study. The analysis forms the basis for future work in Cluster IV and provides cross-cluster information about the status quo of market structures and regulatory framework conditions. In addition to the systematic processing of the market framework, the scientific literature and already published studies on the subject of demand flexibility (demand side management and demand response) are analyzed and summarized in a meta study. The analysis forms the basis for future work in Cluster IV and provides cross-cluster information about the status quo of market structures and regulatory framework conditions. In addition to the systematic processing of the market framework, the scientific literature and already published studies on the subject of demand flexibility (demand side management and demand response) are analyzed and summarized in a meta study

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