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Meta LLaMA 3 70B Instruct

  • Context Window: 8,000
  • TPM: 300,000
  • RPM: 400
  • Embedding Size: NA
Attributes

Dialog systems, Code generation, Following instructions, Sentiment analysis

Meta LLaMA 3 70B Instruct

Meta LLaMA 3 70B Instruct is a cutting-edge large language model designed to support a wide range of applications, from content creation and conversational AI to research and enterprise solutions. As part of the Meta LLaMA 3 series, it serves as a foundational system that fosters innovation and experimentation in the field of generative AI. This model is optimized for high-performance language processing, making it ideal for tasks requiring advanced language understanding and nuanced responses.

Targeted for both commercial and research use, Meta LLaMA 3 70B Instruct excels in delivering accurate and contextually relevant outputs in English. Fine-tuned chat models within this framework are specifically tailored for applications involving conversational AI, enhancing user interaction and engagement. Its robust capabilities in language modeling, dialog systems, and text analysis provide a solid foundation for building sophisticated AI-driven applications and conducting in-depth research.

Supported Use Cases:

  • Language Modeling
  • Dialog Systems
  • Code Generation
  • Following Instructions
  • Sentiment Analysis with Nuances in Reasoning
  • Text Classification with Improved Accuracy and Nuance
  • Text Summarization with Accuracy and Nuance

Meta LLaMA 3 70B Instruct is a powerful tool for developers, researchers, and businesses looking to leverage advanced AI capabilities. Its extensive range of attributes and use cases positions it as a key resource for driving innovation and scaling generative AI solutions across various sectors.

CogniTech AI Credits

Below you will find all supported platforms and the related CogniTech AI Credits costs.

AWS Bedrock Credits

Region Context TPM RPM Input Credits Output Credits
Region Context TPM RPM Input Credits Output Credits
us-west-2 8,000 300,000 400 0.3445 / 1000 tokens 0.455 / 1000 tokens
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