Articles for category: AI Tools

March 30, 2025

ikayaniaamirshahzad@gmail.com

Llama 2 on Amazon SageMaker a Benchmark

Deploying large language models (LLMs) and other generative AI models can be challenging due to their computational requirements and latency needs. To provide useful recommendations to companies looking to deploy Llama 2 on Amazon SageMaker with the Hugging Face LLM Inference Container, we created a comprehensive benchmark analyzing over 60 different deployment configurations for Llama 2. In this benchmark, we evaluated varying sizes of Llama 2 on a range of Amazon EC2 instance types with different load levels. Our goal was to measure latency (ms per token), and throughput (tokens per second) to find the optimal deployment strategies for three

March 30, 2025

ikayaniaamirshahzad@gmail.com

Flexbox Hands-On: A Visual Playground for Learning CSS Layout

Are you tired of wrestling with CSS layouts? Flexbox offers powerful solutions, but there’s a big difference between reading documentation and seeing it in action. That’s why I created an interactive Flexbox Explorer that helps you master this essential layout technique through hands-on experimentation. 🧪 Why Another Flexbox Tool? Learning web development concepts clicks best when you can immediately see the results of your code changes. My Flexbox Explorer bridges theory and practice by letting you: Tweak container properties and see instant layout changes Style individual flex items with custom colors and content Access the exact HTML/CSS code powering what

March 30, 2025

ikayaniaamirshahzad@gmail.com

Train a LLaMA 2 chatbot

In this tutorial we will show you how anyone can build their own open-source ChatGPT without ever writing a single line of code! We’ll use the LLaMA 2 base model, fine tune it for chat with an open-source instruction dataset and then deploy the model to a chat app you can share with your friends. All by just clicking our way to greatness. 😀 Why is this important? Well, machine learning, especially LLMs (Large Language Models), has witnessed an unprecedented surge in popularity, becoming a critical tool in our personal and business lives. Yet, for most outside the specialized niche

March 30, 2025

ikayaniaamirshahzad@gmail.com

The Evolution of Web Component Modules in Raku: A Journey of Diverse Approaches

Over the past several years, the Raku community has explored a variety of approaches to building web interfaces. Each module represents a distinct set of design choices and trade-offs, reflecting different philosophies and priorities. In this post, we’ll review key milestones in the evolution of web component modules in Raku and provide code examples that illustrate how each solution works in practice. 2017 – p6-react p6-react was one of the earliest experiments in bringing a component-based approach to Raku web development. Inspired by modern front-end frameworks, p6-react enables developers to write server-side components with a declarative syntax. The following example

March 30, 2025

ikayaniaamirshahzad@gmail.com

Changelog · Prodigy · An annotation tool for AI, Machine Learning & NLP

Select page…Get Started › Prodigy 101Get Started › Installation & SetupGet Started › ChangelogUsage › Named Entity RecognitionUsage › Span CategorizationUsage › Text ClassificationUsage › Dependencies & RelationsUsage › Computer VisionUsage › Audio & VideoUsage › Task RoutingUsage › Large Language ModelsUsage › ReviewUsage › Custom RecipesUsage › Custom InterfacesUsage › MetricsUsage › DeploymentAPI › RecipesAPI › Annotation InterfacesAPI › Web ApplicationAPI › Loaders & Input DataAPI › Components & FunctionsAPI › DatabasePlugins › Open Source PluginsPlugins › Single Sign-onPlugins › Modal This page lists the history of changes to Prodigy. Whenever a new update is available, you’ll receive an

March 30, 2025

ikayaniaamirshahzad@gmail.com

Hugging Face Goes To Washington and Other Summer 2023 Musings

One of the most important things to know about “ethics” in AI is that it has to do with values. Ethics doesn’t tell you what’s right or wrong, it provides a vocabulary of values – transparency, safety, justice – and frameworks to prioritize among them. This summer, we were able to take our understanding of values in AI to legislators in the E.U., U.K., and U.S., to help shape the future of AI regulation. This is where ethics shines: helping carve out a path forward when laws are not yet in place. In keeping with Hugging Face’s core values of

March 30, 2025

ikayaniaamirshahzad@gmail.com

Introducing Inboto: Your AI Copilot for Effortless Email Support

Transforming Customer Support with AI We’re excited to announce the launch of Inboto, an AI-powered email support assistant designed to help businesses automate their email workflows, respond faster, and improve customer satisfaction. If you’ve ever felt overwhelmed by support emails, Inboto is here to change that. Why We Built Inboto Customer support is time-consuming and repetitive. Many businesses struggle with handling a high volume of support emails while maintaining personalized, on-brand responses. We built Inboto to: Reduce email response times by up to 90% Automate repetitive support inquiries Integrate seamlessly with Stripe for subscription management Enable businesses to create their

March 30, 2025

ikayaniaamirshahzad@gmail.com

Introducing spaCy v2.3 · Explosion

spaCy now speaks Chinese, Japanese, Danish, Polish and Romanian! Version 2.3 of the spaCy Natural Language Processing library adds models for five new languages. We’ve also updated all 15 model families with word vectors and improved accuracy, while also decreasing model size and loading times for models with vectors. This is the last major release of v2, by the way. We’ve been working hard on spaCy v3, which comes with a lot of cool improvements, especially for training, configuration and custom modeling. We’ll start making prereleases on spacy-nightly soon, so stay tuned. spaCy v2.3 provides new model families for five

March 30, 2025

ikayaniaamirshahzad@gmail.com

Finetune Stable Diffusion Models with DDPO via TRL

Diffusion models (e.g., DALL-E 2, Stable Diffusion) are a class of generative models that are widely successful at generating images most notably of the photorealistic kind. However, the images generated by these models may not always be on par with human preference or human intention. Thus arises the alignment problem i.e. how does one go about making sure that the outputs of a model are aligned with human preferences like “quality” or that outputs are aligned with intent that is hard to express via prompts? This is where Reinforcement Learning comes into the picture. In the world of Large Language