Technical specifications of LLaMa 3.2 90B
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Architecture: a transformer model with long-context support, designed to process massive volumes of data.
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Parameter count: 90 billion
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Language support: able to process large amounts of text information and handle complex analytical tasks.
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Applications: suited to demanding scientific research and analysis of large data volumes.
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Distinctive features: support for a context of up to 128K tokens, which makes it indispensable for working with long texts and for deep data analysis
Prompting recommendations for Llama 3.2 90B
Analysis of long texts:
Use the model to process large volumes of text data such as scientific studies, reports or technical articles.
Example:
Analyze the full text of the study and highlight the key conclusions.
Multi-step analytical requests:
The model performs well on multi-step analytical tasks that require sequential processing and analysis of data from different sources.
Example:
Analyze the economic data and produce a five-year forecast, taking all factors into account.
Long-context support:
The model works effectively with large volumes of text such as long reports, books or academic papers, which makes it ideal for academic and research work.
Example:
Analyze the text of the book and suggest how to restructure it for scientific publication.