#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
The euro banknotes feature the name of the currency in how many different scripts?
(hidden behind the cat in the image)
Options:
A) 1
B) 2
C) 3
D) 4
@languagetrivia#script
#Python#script
🤖
PlotAI
PlotAI es una herramienta para generar gráficos en Matplotlib.
— el usuario envía un marco de datos como entrada;
— PlotAI crea un mensaje para LLM, que contiene los primeros cinco registros y genera código Python;
- Se ejecuta el código Python devuelto y se muestra el gráfico.
pip install plotai
🔗Github
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Main channel: @repo_science
Coupons: @freecoupons_reposcience
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🌍 Which African country uses Amharic as an official language?
A) 🇪🇹 Ethiopia
B) 🇪🇷 Eritrea
C) 🇸🇴 Somalia
D) 🇸🇩 Sudan
@languagetrivia#guess_the_country#script
Which language is written in a script called ‘Devanagari’, a script characterized by a horizontal line running along the top of the letters, connecting them?
Follow @languagetrivia🌏 for more!
#script#image#guess_the_language