開源 LLM Post-Training 全攻略：從 SFT 到 RLHF，手把手帶你訓練 Qwen
全面介紹開源 LLM 的 Post-Training 方法，包含 SFT、RLHF、DPO、ORPO、持續預訓練等技術，以 Qwen 為範例，深入分析各方法的優缺點、所需資源與適用場景，幫助你選擇最合適的訓練策略。
Machine Learning
Articles in machine learning
Fine-Tuning LLMs with AWS Bedrock: A Complete Guide to Post-Training Customization
Comprehensive guide to fine-tuning and customizing Large Language Models (LLMs) with AWS Bedrock - covering supervised fine-tuning, continued pre-training, and reinforcement fine-tuning with practical examples and AWS CDK infrastructure setup.
Deploying Hugging Face Models to AWS: A Complete Guide with CDK, SageMaker, and Lambda
🎯 Introduction Deploying machine learning models to production is a complex challenge that goes far beyond training a model. When working with large models from Hugging Face—whether it’s image generation, text-to-image synthesis, or other AI tasks—you need robust infrastructure that handles: Scalability: Auto-scaling to handle variable loads from 0 to thousands of concurrent requests Cost Efficiency: Paying only for what you use while maintaining performance Reliability: 99.9%+ uptime with proper error handling and monitoring Security: Protecting models, data, and API endpoints Observability: Comprehensive logging, metrics, and tracing This comprehensive guide demonstrates how to deploy a Hugging Face model to AWS using infrastructure as code (CDK with TypeScript), combining SageMaker for model hosting and Lambda for API orchestration.