BLOOM is an autoregressive Large Language Model (LLM), trained to continue text from a prompt on vast amounts of text data using industrial-scale computational resources. As such, it is able to output coherent text in 46 languages and 13 programming languages that is hardly distinguishable from text written by humans.

BLOOM Overview. The BLOOM model has been proposed with its various versions through the BigScience Workshop. BigScience is inspired by other open science initiatives where researchers have pooled their time and resources to collectively achieve a higher impact.

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Today, we release BLOOM, the first multilingual LLM trained in complete transparency, to change this status quo — the result of the largest collaboration of AI researchers ever involved in a single research project. With its 176 billion parameters, BLOOM is able to generate text in 46 natural languages and 13 programming languages.

The BLOOM tokenizer is a learned subword tokenizer trained using: A byte-level Byte Pair Encoding (BPE) algorithm . A simple pre-tokenization rule, no normalization. A vocabulary size of 250,680. It was trained on a subset of a preliminary version of the corpus using alpha-weighting per language. Citation

BigScience Large Open-science Open-access Multilingual Language Model (BLOOM) [1] [2] is a 176-billion-parameter transformer-based autoregressive large language model (LLM). The model, as well as the code base and the data used to train it, are distributed under free licences. [3]

BigScience BLOOM is an autoregressive Large Language Model (LLM), trained to continue text from a prompt on vast amounts of text data using industrial-scale computational resources. As such, it is able to output coherent text in 46 languages and 13 programming languages that is hardly distinguishable from text written by humans.

🌸 Introducing The World's Largest Open Multilingual Language Model: BLOOM 🌸 Large language models (LLMs) have made a significant impact on AI research. These powerful, general models can take on a wide variety of new language tasks from a user’s instructions.

The Huggingface Bloom documentation provides an overview of the BLOOM model and its various versions, along with instructions on how to use the model for different tasks using the Huggingface Transformers library.

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