DeepSeek-R1 uses cost-effective Reinforcement Learning to unlock emergent reasoning. By rewarding correct, verifiable steps, it refines logic and answers—showcasing how systematic feedback can reduce data needs and boost performance. Here I discuss my understanding from research paper.
Struggling to finish side projects due to limited time? In my latest blog post, I share how I improved my productivity using AI tools like LLMs and Cursor IDE. Learn to refine specs, bootstrap code, and iterate effectively to rapidly build and deploy your projects—even with a busy schedule.
Learn how to install a custom Hugging Face GGUF model using Ollama, enabling you to try out the latest LLM models locally. This guide covers downloading the model, creating a Modelfile, and setting up the model in Ollama and Open-WebUI.
Explore OpenWebUI's Pipelines: extend your self-hosted LLM interface. Learn to create custom pipelines, from filters to tools. Intercept LLM interactions, implement function-calling, and integrate new providers. Enhance AI workflows or build RAG systems with this guide to OpenWebUI's extensibility.
Discover how to create a synthetic dataset, select the right metrics for evaluation, and fine-tune your model using LoRA for a narrow scenario. Plus, learn how to serve your model efficiently using LLaMa.cpp on Mac/Linux.
