The Importance of Having a Hobby: Unwind, Relax, and Thrive

This is the first time I am writing a blog post on a topic other than technology; it’s about personal well-being. I am a hobby photographer. I love cooking and gardening especially, hydroponics. In our fast-paced digital age, where screens often dominate our attention and work consumes our time, it’s easy to overlook the importance of nurturing our personal passions and interests. Today, I want to delve into a subject close to my heart—hobbies—and explore how they contribute not only to our personal well-being but also to our overall happiness and fulfillment.

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Phi2: Mastering Language Models on Your Desktop with Only 15 Lines of Code

Phi2 chat interface

In the rapidly evolving landscape of AI and machine learning, accessing powerful language models has become easier than ever. With advancements like the llama_cpp Python library and the wealth of models available on platforms like Hugging Face, running sophisticated language models on your local machine is now within reach. In this article, we’ll explore how to unleash the capabilities of a small language model called Phi2 using just 15 lines of Python code.

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SQLCoder2-7B – Text-2-SQL Generation using Yelp Dataset

In the realm of data analysis and manipulation, SQL (Structured Query Language) stands as a cornerstone for interacting with relational databases. However, crafting SQL queries can sometimes be daunting, especially for those less familiar with the language. Enter SQLCoder2-7B, a cutting-edge model that leverages the power of natural language processing (NLP) to generate SQL queries from plain text. In this guide, we’ll explore how to utilize SQLCoder2-7B with the Yelp dataset, diving into the implementation details using llama_cpp and a compatible gguf model from Hugging Face.

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Synthetic Data Generation with Synthetic Data Vault

Synthetic data generation

In the ever-evolving landscape of artificial intelligence (AI) and machine learning, the demand for diverse and expansive datasets has become paramount. Real-world data, while invaluable, often presents challenges such as privacy concerns, limited access, and scalability issues. This is where the concept of synthetic data comes into play, offering an innovative solution to these challenges.

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Great Expectations: An Introduction to In-Memory Context

In the world of data science and data engineering, ensuring the quality and integrity of your data is crucial. One tool that can help with this is Great Expectations, a Python library that allows you to test your data against a set of “expectations”. In this blog post, we’ll explore a particular feature of Great Expectations: the in-memory context.

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