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Showing posts with the label DeepSeq R1

5 DevOps GitHub Actions: Automate Your App & Boost Productivity

Introduction Boost your software project's productivity with automation! This blog post, inspired by a Fireship.io YouTube tutorial, explores five ways to leverage GitHub Actions to streamline your workflow and enhance code quality. We'll cover Continuous Integration (CI), Continuous Deployment (CD), automated releases, and more, transforming your development process with DevOps best practices. What are GitHub Actions? GitHub Actions automates workflows within your GitHub repository. Any event – a pull request, a push to a branch, or even a new repository – can trigger an automated workflow. These workflows run in cloud-based containers, executing a series of steps you define. Instead of writing every step from scratch, you can utilize hundreds of pre-built "actions" contributed by the community...

QWQ32B: The Tiny AI Giant Outperforming DeepSeek R1?

Introduction The AI world is buzzing with the arrival of QWQ32B, a new open-source large language model (LLM) from Alibaba's Quen Team. This 32-billion parameter model is challenging the established order by achieving performance comparable to significantly larger models, such as DeepSeq R1 (671 billion parameters), in reasoning, math, and coding tasks. This blog post delves into the specifics of QWQ32B, its performance benchmarks, and the implications of its open-source nature. QWQ32B: A Closer Look QWQ32B (Quen with Questions) builds upon the earlier QVQ model released in November 2024. Unlike many competitors, it boasts a relatively small parameter count (32.5 billion, with 31 billion non-embedding parameters) and can run on hardware with approximately 24GB of VRAM – a stark contrast to the 1500+ GB needed f...