Reinforcement Learning with Human Feedback (RLHF) скачать в хорошем качестве

Reinforcement Learning with Human Feedback (RLHF)

GPT-4 Summary: Dive into the cutting-edge world of aligning Large Language Models (LLMs) with our comprehensive series, kicking off with a focus on Reinforcement Learning with Human Feedback (RLHF). This crucial session aims to demystify RLHF, a technique pivotal in evolving models like InstructGPT and Llama 2 to be more helpful, honest, and harmless. We'll navigate through the RLHF journey, starting with instruct-tuning pre-trained models, advancing to training a reward model that mirrors human preferences, and culminating in fine-tuning via Reinforcement Learning (RL) to polish model alignment. Our hands-on demonstration will feature the Zephyr-7B-Alpha model and a BERT-style rewards model, guiding you through the process with practical code in a Google Colab notebook environment. Join us to unravel the intricacies of RLHF, understand the selection of policy and reward models, and learn how RL and Proximal Policy Optimization (PPO) can refine LLMs to meet human standards of helpfulness and harmlessness. All code will be provided, ensuring you have the tools to apply these groundbreaking techniques in your own projects. Join us every Wednesday at 1pm EST for our live events. SUBSCRIBE NOW to get notified! Speakers: ​Dr. Greg, Co-Founder & CEO AI Makerspace   / gregloughane   The Wiz, Co-Founder & CTO AI Makerspace   / csalexiuk   Apply for The AI Engineering Bootcamp on Maven today! https://bit.ly/AIEbootcamp LLM Foundations - Email-based course https://aimakerspace.io/llm-foundations/ For team leaders, check out! https://aimakerspace.io/gen-ai-upskil... Join our community to start building, shipping, and sharing with us today!   / discord   How'd we do? Share your feedback and suggestions for future events. https://forms.gle/z96cKbg3epXXqwtG6 00:00:00 Introduction to AI Maker Space Event 00:03:50 Aligning Large Pre-trained Models for Task Optimization 00:07:36 Understanding Fine-Tuning and Reward Models in AI 00:11:33 Understanding SFT Model Performance 00:15:29 Evaluating Model Harmlessness with Real Toxicity Dataset 00:18:50 Setting Up a Transformers Text Generation Pipeline 00:22:21 Using Pre-Trained Models for Policy Optimization 00:26:29 Training Zephyr for Safe AI Responses 00:30:15 Training Methods for Reward Models 00:33:53 Optimizing Training Iterations and Sample Size 00:37:12 Setting Up Auto Model for Reward Integration 00:40:50 Optimizing Model Training with PO Training Loop 00:44:24 Optimizing Models Using RLHF in Alignment Strategies 00:48:59 The Role of RHF in Industry Alignment 00:51:34 Cost-Effective Fine-Tuning with Quantization and LoRA 00:55:08 Best Practices for Data Set Integration 00:58:53 Feedback and Future Goals for 2024