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Simone Gambarini on Muck Rack

Simone Gambarini

As seen in: Zephyrnet
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Articles

Efficient Continual Pre-training LLMs For Financial Domains

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By Enrico Zanetti, Christopher Peters, Simone Gambarini, Mike Fridgen
| Zephyrnet
Large language models (LLMs) are generally trained on large publicly available datasets that are domain agnostic. For example, Meta’s Llama models are trained on datasets such as CommonCrawl, C4, Wikipedia, and ArXiv. These datasets encompass a broad range of topics and domains.
Open in Who Shared Issue with article?

LLM eficientes y continuos con formación previa para ámbitos financieros

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By Simone Gambarini, Enrico Zanetti, Christopher Peters, Mike Fridgen
| Zephyrnet
Los modelos de lenguajes grandes (LLM) generalmente se entrenan en grandes conjuntos de datos disponibles públicamente que son independientes del dominio. Por ejemplo, La llama de Meta Los modelos se entrenan en conjuntos de datos como Rastreo común, C4, Wikipedia y ArXiv. Estos conjuntos de datos abarcan una amplia gama de temas y dominios.
Open in Who Shared Issue with article?

Efficient Continual Pre-training LLMs For Financial Domains

|
By Simone Gambarini, Enrico Zanetti, Christopher Peters, Mike Fridgen
| Zephyrnet
Large language models (LLMs) are generally trained on large publicly available datasets that are domain agnostic. For example, Metina lama models are trained on datasets such as CommonCrawl, C4, Wikipedia, and ArXiv. These datasets encompass a broad range of topics and domains.
Open in Who Shared View Duplicates (6) Issue with article?
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