A practical guide to selecting, optimizing, and operating small language models for edge deployment—latency, memory, tooling, and MLOps.
Step-by-step QLoRA guide with concepts, setup, memory tips, and code to fine-tune LLMs using 4-bit quantization on a single GPU.
Hands-on knowledge distillation tutorial for compact models: concepts, PyTorch/Keras code, tuning tips, and deployment with quantization.
A practical guide to integrating TensorFlow Lite models into Flutter for fast, private, offline on-device AI with performance tuning and code examples.
Build and deploy an edge AI model on-device: train, quantize to TFLite, and run on Raspberry Pi and Android with real-time profiling and optimization.
Practical, end-to-end guide to deploying open-source LLMs—from model choice and hardware sizing to serving, RAG, safety, and production ops.
