BioMed-RoBERTa-base is a language model based on the RoBERTa-base (Liu et. al, 2019) architecture. We adapt RoBERTa-base to 2.68 million scientific papers from the Semantic Scholar corpus via continued pretraining. This amounts to 7.55B tokens and 47GB of data. We use the full text of the papers in training, not just abstracts. Specific details of the adaptive pretraining procedure can be found in Gururangan et. al, 2020. BioMed-RoBERTa achieves competitive performance to state of the art models on a number of NLP tasks in the biomedical domain (numbers are mean (standard deviation) over 3+ random seeds)

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Bolmo-1B

allenai

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Bolmo-1B-Stage1

allenai

OLMo 2 1B retrofitted to operate over bytes instead of tokens via *byteification* through a short additional training procedure. **This checkpoint contains Stage 1 training only - inner model parameters are unchanged.** Like any base language model or fine-tuned model without safety filtering, these models can easily be prompted by users to generate harmful and sensitive content. Such content may also be produced unintentionally, especially in cases involving bias, so we recommend that users consider the risks when applying this technology. Additionally, many statements from Bolmo or any LLM are often inaccurate, so facts should be verified.

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Bolmo-7B

allenai

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Bolmo-7B-Stage1

allenai

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Bwen-8B

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Bwen-8B-Stage1

allenai

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cosmo-xl

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cs_roberta_base

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DataDecide-c4-10M

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DataDecide-c4-150M

allenai

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