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.
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.
End-to-end guide: fine-tune Mistral models with LoRA using Hugging Face Transformers, then deploy at scale with vLLM on AWS â from training to production serving on SageMaker, ECS, or Bedrock.