Articles for category: AI Tools

March 29, 2025

ikayaniaamirshahzad@gmail.com

Optimum-NVIDIA Unlocking blazingly fast LLM inference in just 1 line of code

Large Language Models (LLMs) have revolutionized natural language processing and are increasingly deployed to solve complex problems at scale. Achieving optimal performance with these models is notoriously challenging due to their unique and intense computational demands. Optimized performance of LLMs is incredibly valuable for end users looking for a snappy and responsive experience, as well as for scaled deployments where improved throughput translates to dollars saved. That’s where the Optimum-NVIDIA inference library comes in. Available on Hugging Face, Optimum-NVIDIA dramatically accelerates LLM inference on the NVIDIA platform through an extremely simple API. By changing just a single line of code,

March 29, 2025

ikayaniaamirshahzad@gmail.com

Keep Users on Track: The Importance of Visible Progress in UX

Progress visibility is a fundamental aspect of UX design. It reassures users, keeps them engaged, and helps them navigate tasks smoothly. When users understand where they are in a process, they feel more in control and are more likely to complete their journey successfully. Why Making Progress Visible Matters A well-designed progress indicator offers multiple benefits: Reduces user anxiety – Uncertainty can frustrate users. When they see tangible progress, they feel more at ease and confident. Boosts engagement – A clear sense of achievement encourages users to stay committed to a task. Minimizes drop-offs – Users are more likely to

March 29, 2025

ikayaniaamirshahzad@gmail.com

Goodbye cold boot – how we made LoRA Inference 300% faster

tl;dr: We swap the Stable Diffusion LoRA adapters per user request, while keeping the base model warm allowing fast LoRA inference across multiple users. You can experience this by browsing our LoRA catalogue and playing with the inference widget. In this blog we will go in detail over how we achieved that. We’ve been able to drastically speed up inference in the Hub for public LoRAs based on public Diffusion models. This has allowed us to save compute resources and provide a faster and better user experience. To perform inference on a given model, there are two steps: Warm up

March 29, 2025

ikayaniaamirshahzad@gmail.com

🚀 Debouncing in JavaScript & React — Make Your App Smoother!

👋 Hey folks! I’m Rajat Sharma, and today we’re diving into an underrated but powerful concept: Debouncing. Have you ever typed in a search bar and noticed how some apps wait a moment before responding? That’s not lag — it’s debouncing, and it’s intentional. In this article, you’ll learn: What debouncing is Why it matters in modern web apps How to implement it in React (with real API examples!) And the trade-off it brings Let’s go! 🧠✨ 🧩 What is Debouncing? Debouncing is a technique used to delay the execution of a function until a certain time has passed since

March 29, 2025

ikayaniaamirshahzad@gmail.com

Introducing spaCy v3.0 · Explosion

spaCy v3.0 is a huge release! It features new transformer-based pipelines that get spaCy’s accuracy right up to the current state-of-the-art, and a new workflow system to help you take projects from prototype to production. It’s much easier to configure and train your pipeline, and there are lots of new and improved integrations with the rest of the NLP ecosystem. We’ve been working on spaCy v3.0 for over a year now, and almost two years if you count all the work that’s gone into Thinc. Our main aim with the release is to make it easier to bring your own

March 29, 2025

ikayaniaamirshahzad@gmail.com

Few-Shot Aspect Based Sentiment Analysis using SetFit

SetFitABSA is an efficient technique to detect the sentiment towards specific aspects within the text. Aspect-Based Sentiment Analysis (ABSA) is the task of detecting the sentiment towards specific aspects within the text. For example, in the sentence, “This phone has a great screen, but its battery is too small”, the aspect terms are “screen” and “battery” and the sentiment polarities towards them are Positive and Negative, respectively. ABSA is widely used by organizations for extracting valuable insights by analyzing customer feedback towards aspects of products or services in various domains. However, labeling training data for ABSA is a tedious task

March 29, 2025

ikayaniaamirshahzad@gmail.com

Leetcode 148 : Sort List

https://leetcode.com/problems/sort-list/description/ Given the head of a linked list, return the list after sorting it in ascending order. Example 1: Input: head = [4,2,1,3]Output: [1,2,3,4]Example 2: Input: head = [-1,5,3,4,0]Output: [-1,0,3,4,5]Example 3: Input: head = []Output: [] The number of nodes in the list is in the range [0, 5 * 104].-10^5 <= Node.val <= 10^5 This is basically to sort the linked list using some sorting method. We can store this in a list, sort it using a library function, and reconstruct the linked list. This is correct, but we are not fully utilising the fact that the question is

March 29, 2025

ikayaniaamirshahzad@gmail.com

spaCy v3: Custom trainable relation extraction component

spaCy v3.0 features new transformer-based pipelines that get spaCy’s accuracy right up to the current state-of-the-art, and a new training config and workflow system to help you take projects from prototype to production. In this video, Sofie shows you how to apply all these new features when implementing a custom trainable component from scratch. Source link

March 29, 2025

ikayaniaamirshahzad@gmail.com

Mixture of Experts Explained

With the release of Mixtral 8x7B (announcement, model card), a class of transformer has become the hottest topic in the open AI community: Mixture of Experts, or MoEs for short. In this blog post, we take a look at the building blocks of MoEs, how they’re trained, and the tradeoffs to consider when serving them for inference. Let’s dive in! Table of Contents TL;DR MoEs: Are pretrained much faster vs. dense models Have faster inference compared to a model with the same number of parameters Require high VRAM as all experts are loaded in memory Face many challenges in fine-tuning,