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

Finite element analysis of aircraft tyres

In this thesis, the Finite Element (FE) Analysis of aircraft tyres is presented. The modelling and simulations of detailed construction of tyre enable tyre manufacturers to evaluate new designs and development before a prototype is fabricated, and aim to reduce the research costs and efforts to optimise the current tyre design of tyres. The material properties is key in the FE modelling and analysis, a number of sample from the rubber compounds and reinforcement were used to characterise the elastic, hyperelastic and viscoelastic behaviour of material. In this research, two aircraft tyres were employed for correlation study to a number of design checks and standard tests such as profile growth, sidewall deformation, footprint, contact pressure, and load-deformation data. The burst simulation was carried out to investigate when and where the failure in tyre occurs and compares it with what happens in real-world burst test as an important procedure in tyre safety certification by aviation authorities. As a result, the virtual testing would shorten the design procedure by checking the design parameters in advance of tyre prototyping. Moreover, the FE parameters such as mesh size and tyre geometry are investigated for optimisation of the runtime and accuracy and improvements in the FE results. A number of simulations were run to determine generated forces and moments across the contact patch using a steady-state approach in presence of air as the tyre inflator to obtain a higher accuracy in prediction of the vertical stiffness and footprint area. In addition, the tyre was freely rolled on the runway using an explicit approach to investigate the energy dissipation and heat build-up per tyre rotation due to the tyre viscoelasticity. Finally, TAIS (Tyre Analysis Interface System) development is explained in response to the design requirements from Dunlop Aircraft Tyres

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