Two strategies to shrink LLMs â one compresses weights, the other transfers knowledge. A practical guide to distillation and quantization: when to use each, how to implement them with Hugging Face, and why the real answer is both.
A practical walkthrough of two paths to working with Mistral â the managed API for fast prototyping and self-hosted deployment for full control â with real code covering prompting, model selection, function calling, RAG, and INT8 quantization.
Everything a cloud/AWS engineer needs to know about Python, the Hugging Face Transformers framework, SageMaker integration, quantization, CUDA, and AWS Inferentia â without being a data scientist.