Llama3.3
Meta's latest Llama 3.3 model achieves performance comparable to that of larger models with fewer parameters, providing an efficient and low-cost AI text solution.
Core content:
1. Llama 3.3 model parameter and performance comparison
2. Multi-language support and architecture optimization
3. Security measures: Llama Guard 3, Prompt Guard, Code Shield
Meta released Llama 3.3 on December 6, 2024, with a total of 70 billion parameters (70B), and its performance is comparable to that of Llama 3.1, which has 405 billion parameters. Multiple test results and performance are close to GPT-4o
Target:
The Llama 3.3 model is more efficient and less expensive and can run on standard workstations, reducing operating costs while providing high-quality text AI solutions.
The Llama 3.3 model focuses on optimizing multi-language support, supporting eight languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.
Model:
In terms of architecture, Llama 3.3 is an auto-regressive language model that uses an optimized transformer decoder architecture. 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.
In terms of security, Meta adopts measures such as data filtering, model fine-tuning, and system-level security protection to reduce the risk of model abuse. In addition, Meta encourages developers to take necessary security measures when deploying Llama 3.3, such as:
Llama Guard 3:
Function: Monitor and filter input and output to prevent the generation of harmful or inappropriate content.
Role: Ensure that model outputs meet safety and ethical standards.
Prompt Guard:
Function: Detect and block malicious or inappropriate input prompts.
Purpose: Prevent users from inducing the model to generate harmful content through specific prompts.
Code Shield:
Function: Prevent the model from generating malicious code or performing unsafe operations.
Purpose: Ensure the security of the generated code to prevent it from being used for attacks or vulnerability exploits.
Training data volume: 15 trillion (T, Trillion) public data
Knowledge Deadline: 2023.12
Training cost: 39.3 million GPU hours (H100-80G)
Llama 3.3 uses a new alignment process and improved online reinforcement learning (online RL) technology, which enables it to achieve performance levels comparable to Llama 3.1 405B with 70B parameters.
New Alignment Process : Ensure that model outputs are consistent with human intentions and values, improving safety and controllability.
Alignment: refers to the process of aligning the model’s behavior with human intentions, values, or specific goals.
Optimization points:
Finer instruction fine-tuning: Fine-tune the model through higher-quality human feedback data (such as human preference data) to make its output more in line with human expectations.
Multi-objective optimization: Optimizing multiple objectives (such as accuracy, security, fairness) simultaneously, rather than just a single performance metric.
Dynamic alignment: Continuously monitor and adjust models after they are deployed to ensure they always behave as expected.
Function: Improve the usefulness, safety, and controllability of the model and reduce harmful or unexpected outputs.
Improved Online Reinforcement Learning (Online RL) : Optimize the model through real-time user feedback so that it continues to improve after deployment while maintaining high performance.
Reinforcement Learning (RL): A machine learning method in which a model optimizes its behavior based on reward signals by interacting with its environment.
Online reinforcement learning (Online RL): After the model is deployed, it learns and improves from user interactions in real time.
Optimization points:
Real-time feedback: Dynamically adjust the model through user interaction data (such as likes, corrections, and feedback) to make its output more in line with user needs.
Efficient learning: Use more efficient algorithms to reduce reliance on large amounts of data while avoiding degradation of model performance.
Safety constraints: Safety constraints are added during the reinforcement learning process to prevent the model from learning harmful behaviors.
Purpose: Enables the model to continuously improve and adapt to diverse user needs while maintaining high performance and security.
In summary, Llama 3.3 achieves comparable performance to larger models with fewer parameters while being more secure and adaptable.
