Articles | Notes | Tips | Tutorials
In this short quick tip article, we will demonstrate how one can run the ‘Flux.1-Schnell‘ text-to-image Diffusion model on a local machine (either a desktop with 16gb NVidia GPU or an Apple Silicon Mac) to generate an image given an input prompt.
Here is the link to the short quick tip article:
Quick Tip :: Running Text-to-Image Diffusion Model on a Local Machine
P.S: This is the 400th article post on PolarSPARC !!!
Happy New Year – Enjoy 🙂 !!!
In this article, we will provide code snippets for commonly performed tasks using the popular LLM framework LangChain.
Here is the link to the article on common LangChain recipes:
Enjoy 🙂 !!!
In Part-3 of this primer series, we will enhance the custom Rasa helper chatbot such that, it is configured, trained, and deployed once, to leverage data from an external source for future knowledge expansion.
Here is the link to the article on enhanced Rasa custom helper chatbot:
Hands-on Primer on Rasa – Part 3
Enjoy 🙂 !!!
Most of us have been using some form of a badge or tag to enter the office building OR tap a credit card to make a payment at the checkout.
Ever wondered what is really going on behind the scene when we tap a card on the card reader ???
This is where the RFID/NFC technology comes into play. We will leverage a Raspberry Pi 4 along with a Mifare RC522 RFID module to get our hands dirty. Here is the link to the article demonstrating the concept of RFID/NFC:
Enjoy 🙂 !!!
In this article, we will understand the basics of model Quantization which allows one to shrink the size of any pre-trained LLM model.
Here is the link to the article on Quantization:
Understanding Model Quantization
Enjoy 🙂 !!!
In this primer, we will provide an overview of the popular LLM framework LangChain AND get our hands dirty with some code samples on the various core components.
Here is the link to the article on LangChain:
Enjoy 🙂 !!!
Pandas DataFrame is an Excel like table structure, with rows and columns of data, and is extensively used for slicing and dicing of data for analysis. It provides in-built support for styling so that data can be presented in a more meaningful way.
Here is the link to the article on Pandas DataFrame Styling:
Enjoy 🙂 !!!
In this primer, we will provide an overview of the Hugging Face platform as well as get our hands dirty with some code samples on the various text processing tasks.
Here is the link to the article on Hugging Face:
Enjoy 🙂 !!!
In this part of the Deep Learning series, we will introduce the concept of the Sequence-to-Sequence (also known as the Encoder-Decoder) model in the context of a Language Translation use-case. In addition, we will get our hands dirty to demonstrate the translation of English sentences to Spanish sentences. To accomplish this task, we will implement an GRU model using PyTorch.
Here is the link to the article on Deep Learning demonstrating the English-to-Spanish Language Translation using GRU:
Deep Learning – Sequence-to-Sequence Model
Enjoy 🙂 !!!
In this part of the Deep Learning series, we will explain how a Gated Recurrent Unit (GRU) network works and use PyTorch for a practical demonstration to predict the Next Word.
Here is the link to the article on Deep Learning using GRU:
Deep Learning – Gated Recurrent Unit
Enjoy 🙂 !!!