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Gemma-2 + RAG + LlamaIndex + VectorDB

Open in: 1. Introduction Retrieval-Augmented Generation (RAG) is an advanced AI technique that enhances large language models (LLMs) with the ability to access and utilize external knowledge. This guide will walk you through a practical implementation of RAG using Python and various libraries, explaining each component in detail. 2. Setup and Import %pip install transformers accelerate bitsandbytes flash-attn faiss-cpu llama-index -Uq %pip install llama-index-embeddings-huggingface -q %pip install llama-index-llms-huggingface -q %pip install llama-index-embeddings-instructor llama-index-vector-stores-faiss -q import contextlib import os import torch device = torch.

Sunday, July 14, 2024 | 14 minutes Read
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Florence-2 - Vision Foundation Model - Examples

Install dependencies Type the following command to install possible needed dependencies (especially if the inference is performed on the CPU) %pip install einops flash_attn In Kaggle, transformers and torch are already installed. Otherwise you also need to install them on your local PC. Import Libraries from transformers import AutoProcessor, AutoModelForCausalLM from PIL import Image import requests import copy import torch %matplotlib inline Import the model We can choose Florence-2-large or Florence-2-large-ft (fine-tuned).

Tuesday, June 25, 2024 | 5 minutes Read
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MSFT Stock Prediction using LSTM or GRU

Introduction In this article, we will explore time series data extracted from the stock market, focusing on prominent technology companies such as Apple, Amazon, Google, and Microsoft. Our objective is to equip data analysts and scientists with the essential skills to effectively manipulate and interpret stock market data. To achieve this, we will utilize the yfinance library to fetch stock information and leverage visualization tools such as Seaborn and Matplotlib to illustrate various facets of the data.

Sunday, June 16, 2024 | 6 minutes Read