#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
#GLMR/USDT analysis :
#GLMR is in downtrend trading below 200ema. Price has broken down below support zone it is expected to continue declining. For short entry wait for the price to test the resistance zone.
TF : 2h
Entry : $0.1186
Target : $0.0940
SL : $0.1332
#GLMR/USDT analysis :
#GLMR has broken below the 200 EMA and is currently consolidating sideways near the 200 EMA. The price is expected to reverse from there and continue its bearish momentum to test the previous swing low.
TF : 1h
Entry : $0.1547
Target : $0.1415
SL : $0.1635
#GLMR👈
Call given here
Hit 1474
All Target done ✅
142% safe profit in spot
If you invested 1 btc it's now 2.42 btc now 🤑🤑
Signal before pump @ low level
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#GLMR👈
https://www.binance.com/en/trade/GLMR_BTC
Buying Zone 610-630
Coin on the brink of 🚀
Buy in parts 👈strictly follow for max profits as its 🚀
Sell
🤑 660-700
🤑 700-750
🤑 750-800
🚀 800- 750 & above
Bullish above -600