LLMOps（Large Language Model Operations）是一個涵蓋了大型語言模型（如 OpenAI 的 GPT 系列、Google 的 PaLM 以及眾多如 Meta 的 LLaMa 這樣的開源 LLMs）開發、部署、維護和優化的一整套實作的方法論和資料管理流程。LLMOps 的存在目標是確保讓開發者甚至一般使用者能夠高效能、高擴展性且安全地使用這些強大的 AI 模型來建構和營運能夠解決使用者問題的應用程式。整個架構牽涉到 #模型訓練、#微調、#RAG、部署、監控、版本控制、更新、安全性、可協作性、可轉移性以及合規性等各個面向。
Understanding ‘Encoders’ Visual Encoders Training Neural Networks Pre-trained Encoders Common Datasets for Pre-training Types of Pre-trained Visual Encoders Fine-tuning & Transfer Learning Applications Challenges & Considerations Conclusion Pre-trained visual encoders leverage prior knowledge from extensive datasets to offer a head start when tackling new visual tasks. They encapsulate a form of “transfer learning,” allowing models
Basic Understanding of Modality In the context of AI, a “modality” refers to a specific type of data or way of interacting. For example, text, images, audio, and videos are different modalities. Each modality offers a unique representation of information. What is Multi-Modality? Multi-modality involves combining information from multiple modalities to improve AI’s understanding, representation,
