Next-Gen AI Development with Hugging Face

Master the essential skills to build production-ready applications powered by large language models in this course. You'll learn to control text generation with precision using sampling parameters and stopping criteria, design effective prompts with chat templates for instruction-tuned models, build retrieval-augmented generation (RAG) pipelines that enable LLMs to access external knowledge, and extract structured data through constrained generation and function calling. What makes this course unique is its hands-on approach to practical LLM application development. You'll work directly with popular open-source models like Llama, Mistral, and Phi, progressing from basic text generation to sophisticated agent systems. Unlike theoretical courses, you'll build real systems—a semantic search engine with sentence-transformers, a complete RAG-powered question-answering pipeline, and tool-using agents that can execute functions based on LLM reasoning. Whether you're developing chatbots, automating information extraction, or building AI assistants, this course equips you with battle-tested patterns and techniques used in production LLM systems. You'll gain the confidence to choose the right approach for your use case and the skills to implement it reliably using the Hugging Face ecosystem.

Master the complete fine-tuning pipeline—from transformer internals to production deployment—using memory-efficient techniques that run on consumer hardware. This course transforms you from someone who uses large language models into someone who customizes them. You'll learn to fine-tune 7-billion parameter models on a laptop GPU using QLoRA, which reduces memory requirements from 56GB to just 4GB through intelligent quantization and low-rank adaptation. What sets this course apart is its rigorous, scientific approach. You'll apply Popperian falsification methodology throughout: instead of asking "does my model work?", you'll systematically try to break it. This skeptical mindset—testing tokenization edge cases, running rank ablation studies, and validating corpus quality through six falsification categories—builds the critical thinking skills that separate production-ready engineers from those who ship fragile systems. By course end, you'll confidently: calculate VRAM requirements and select appropriate hardware; trace inference through the six-step transformer pipeline; configure LoRA rank to match task complexity; build quality training corpora using AST extraction; and publish datasets to HuggingFace with proper splits and documentation. Built entirely on a sovereign AI stack, everything runs locally with no external dependencies—true ML independence.

Learn to deploy ML models to production using the Sovereign Rust Stack—a pure Rust implementation with zero Python runtime dependencies. This hands-on course teaches you to work with three critical model formats (GGUF, SafeTensors, APR), implement MLOps pipelines with CI/CD and observability, and deploy models across GPU, CPU, WebAssembly, and edge targets. Through real-world projects including a Python-to-Rust transpiler (Depyler), browser-based speech recognition (Whisper.apr), and LLM inference benchmarking (Qwen), you'll master format conversion, cryptographic model signing, and performance optimization. The course culminates in a capstone project deploying Qwen2.5-Coder across all three formats with benchmarking. What makes this course unique: instead of relying on Python frameworks, you'll build with production-grade Rust tooling that compiles to native binaries and WebAssembly. Learn to run sub-millisecond inference in browsers, bundle models into executables, and achieve 2x performance gains over standard tools. Ideal for ML engineers and software developers ready to move beyond notebooks into production deployment.