#jupyter_notebook
Retrieval Augmented Generation (RAG) helps large language models (LLMs) answer questions using up-to-date or private information by connecting them to external data sources, unlike fine-tuning which retrains the model on specific data. RAG is useful when you need current, dynamic information without costly retraining, making it ideal for tasks like customer support or knowledge management. Fine-tuning is better for deep expertise in a specialized field but requires more data and effort. Using RAG lets you get accurate, relevant answers quickly by combining the model’s language skills with fresh, specific data, improving usefulness and reliability.
https://github.com/langchain-ai/rag-from-scratch
251126 || DispatchJapan 𝕏 UPDATE
Every member slayed in their own way. who caught your eye first? 👀🔥
#IDLE#아이들#kpop#dispatch
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── @GIDLE_UPDATE ──
🇬🇧Dispatch #1-2
🇷🇺Диспетчер #1-2
Серия из четырех цифровых комиксов, выпущенных AdHoc Studio в составе Deluxe Edition игры Dispatch
🗣еще 2 выпуска заливать или не нравится такое❓
#комикс#comics#диспетчер#dispatch
#adhoc_studio
https://www.python.org/dev/peps/pep-0443/
This PEP proposes a new mechanism in the #functools standard library module that provides a simple form of generic programming known as #single_dispatch#generic functions.
A generic function is composed of multiple functions implementing the same operation for different types. Which implementation should be used during a call is determined by the #dispatch algorithm. When the implementation is chosen based on the type of a single argument, this is known as #single_dispatch .
#overloading