Llama 3.3

Name: Llama 3.3 Introduction Video
Uploaded: 2025-04-26T13:25:04Z
Duration: 1 min 33 s
Description: Advanced multilingual AI model with enhanced performance and efficiency for diverse applications.

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Advanced multilingual AI model with enhanced performance and efficiency for diverse applications.

Multilingual support

Enhanced performance

Instruction tuned

Optimized efficiency

Reasoning improvement

Mathematical understanding

Complex task handling

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About Llama 3.3

Launched Jan 22, 2025

Introduction Video

Description

Advanced multilingual AI model with enhanced performance and efficiency for diverse applications.

Llama 3.3 is a 70-billion parameter, instruction-tuned AI model optimized for text-based tasks. It offers improved performance in reasoning, mathematical understanding, and instruction following compared to previous versions. Llama 3.3 supports multiple languages and excels in complex tasks while requiring fewer computational resources than larger models

Llama 3.3 Key Features

128k token context window
Enhanced multilingual capabilities
Improved code understanding and generation
Advanced reasoning and mathematical skills
Synthetic data generation
Reinforcement Learning from Human Feedback (RLHF)
Llama Guard safety framework

Llama 3.3 Use Cases

Multilingual customer support and chatbots
Code generation and software development assistance
Content creation and editing across languages
Research and experimentation in AI
Knowledge-based applications (e.g., question answering, summarization)
Synthetic data generation for various applications

Pros

Advanced multilingual capabilities supporting multiple languages.
Improved performance in reasoning and mathematical understanding.
Optimized for text-based tasks and excels in complex applications.
Instruction-tuned for better efficiency in task execution.
Requires fewer computational resources compared to larger models.

Cons

Some users may still prefer models with even larger parameters for extremely specific tasks.
Despite optimizations, may still require significant resources for deployment.
Limited feedback on real-world application scenarios compared to more established models.