#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
🕵️#MEGA Bubblemaps: MEGA tokens were distributed among 8,360 addresses, with approximately 50% of addresses still holding all the tokens. About 40% of addresses have sold them completely, and around 10% of addresses have sold only a portion of the coins. link
Guillaume Faye left us on March 6, 2019, at the age of 69. He was a unique figure who leaves behind an extensive body of work. Like the ancient Greek heroes of old, he will achieve immortality if we continue to share his ideas. Guillaume laid the foundations—now it is up to us, dear comrades, to build the sanctuary of European civilization!
#MEGA
¿Que puede hacer este bot?
@MegaUploadXbot
Este bot cargará archivos y vinculará directamente a su cuenta de Mega entre otras características.
Idioma: inglés
(Visto en @botsgram_cu)
#mega