{"id":24673,"date":"2024-12-08T02:02:46","date_gmt":"2024-12-07T18:02:46","guid":{"rendered":"https:\/\/www.1ai.net\/?p=24673"},"modified":"2024-12-07T22:05:32","modified_gmt":"2024-12-07T14:05:32","slug":"meta-%e4%bb%8a%e5%b9%b4%e5%8e%8b%e8%bd%b4%e5%bc%80%e6%ba%90-ai%e6%a8%a1%e5%9e%8b-llama-3-3-%e7%99%bb%e5%9c%ba%ef%bc%9a700-%e4%ba%bf%e5%8f%82%e6%95%b0%ef%bc%8c%e6%80%a7%e8%83%bd%e6%af%94%e8%82%a9-4050","status":"publish","type":"post","link":"https:\/\/www.1ai.net\/en\/24673.html","title":{"rendered":"Meta's grand finale of the year, the open source AI model Llama 3.3, is on the scene: 70 billion parameters, performance comparable to 405 billion."},"content":{"rendered":"

Meta<\/a> The grand finale of this year's AI models is here.Meta was released yesterday (December 6) Llama<\/a> 3.3.There are 70 billion parameters, but performance is comparable to Llama 3.1 with 405 billion parameters.<\/strong><\/p>\n

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Meta emphasizes that Llama 3.3 models are more efficient, less expensive, and can run on standard workstations, reducing operational costs while delivering high-quality text AI solutions.<\/p>\n

The Llama 3.3 model focuses on optimizing multi-language support, with support for 8 languages: English, German, French, Italian, Portuguese, Hindi, Spanish and Thai.<\/p>\n

In terms of architecture, Llama 3.3 is an autoregressive (autoregressive) language model that uses an optimized transformer architecture, and its fine-tuned version uses Supervised Fine-Tuning (SFT) and Reinforcement Learning Based on Human Feedback (RLHF) to align it with human preferences for usefulness and safety.<\/p>\n

Llama 3.3 contexts are 128K in length, support multiple tool usage formats, and can be integrated with external tools and services to extend the functionality of the model.<\/p>\n

In terms of security, Meta employs data filtering, model fine-tuning, and system-level security to reduce the risk of model misuse; in addition, Meta encourages developers to deploy Llama 3.3 with the necessary security measures, such as Llama Guard 3, Prompt Guard, and Code Shield, to ensure responsible use of the model.<\/p>\n

1AI Attach reference address<\/span><\/p>\n