Llama 3.3: Meta AI Releases New Text-Based Language Model

Llama 3.3: Meta AI Releases New Text-Based Language Model

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🚀 Quick Read

  1. Model Parameters: Llama 3.3 has 70B parameters, comparable to the 405B parameters of Llama 3.1.
  2. Multilingual Support: Supports input and output in 8 languages including English, German, French, etc.
  3. Application Scenarios: Suitable for chatbots, customer service automation, language translation, and various other scenarios.

Main Content

What is Llama 3.3

Llama 3.3: Meta AI Releases New Text-Based Language Model
WeChat Official Account: Oyster Oil Canola – Llama 3.3 – Meta AI’s New Text-Based Language Model

Llama 3.3 is a large multilingual pre-trained language model with 70B parameters launched by Meta AI. This model’s performance is comparable to the 405B parameter Llama 3.1 and is optimized for multilingual conversation, supporting English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.

Llama 3.3 features a longer context window and multilingual input/output capabilities, enabling integration with third-party tools to expand functionality, suitable for commercial and research purposes.

Main Features of Llama 3.3

  • Efficiency and Cost: The Llama 3.3 model is more efficient and cost-effective, capable of running on standard workstations, reducing operating costs while providing high-quality text AI solutions.
  • Multilingual Support: Supports 8 languages including English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai, capable of handling input and output in these languages.
  • Long Context Window: The model supports a context length of 128K.
  • Integration with Third-Party Tools: Integrates with third-party tools and services, expanding functionality and application scenarios.

Technical Principles of Llama 3.3

  • Pre-training and Fine-tuning: Based on the Transformer architecture, it undergoes large-scale pre-training and fine-tuning based on instructions to improve the model’s ability to follow instructions and align with human preferences.
  • Autoregressive Model: As an autoregressive language model, Llama 3.3 predicts the next word based on previous words when generating text, gradually building the output.
  • Reinforcement Learning from Human Feedback (RLHF): A fine-tuning technique where the model learns from human feedback to better align with human preferences for usefulness and safety.

Resources

  • HuggingFace Model Hub: https://huggingface.co/collections/meta-llama/llama-33

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