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

Genetic rearrangements of variable di-residue (RVD)-containing repeat arrays in a baculoviral TALEN system

Virus-derived gene transfer vectors have been successfully employed to express the transcription activator-like effector nucleases (TALENs) in mammalian cells. Since the DNA-binding domains of TALENs consist of the variable di-residue (RVD)-containing tandem repeat modules and virus genome with repeated sequences is susceptible to genetic recombination, we investigated several factors that might affect TALEN cleavage efficiency of baculoviral vectors. Using a TALEN system designed to target the AAVS1 locus, we observed increased sequence instability of the TALE repeat arrays when a higher multiplicity of infection (MOI) of recombinant viruses was used to produce the baculoviral vectors. We also detected more deleterious mutations in the TALE DNA-binding domains when both left and right TALEN arms were placed into a single expression cassette as compared to the viruses containing one arm only. The DNA sequence changes in the domains included deletion, addition, substitution, and DNA strand exchange between the left and right TALEN arms. Based on these observations, we have developed a protocol using a low MOI to produce baculoviral vectors expressing TALEN left and right arms separately. Cotransduction of the viruses produced by this optimal protocol provided an improved TALEN cleavage efficiency and enabled effective site-specific transgene integration in human cells

Baculoviral transduction facilitates TALEN-mediated targeted transgene integration and Cre/LoxP cassette exchange in human-induced pluripotent stem cells

10.1093/nar/gkt721Nucleic Acids Research4119e180-NARH

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

Erratum to: Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition) (Autophagy, 12, 1, 1-222, 10.1080/15548627.2015.1100356

non present