quantizing-models-bitsandbytes

This skill quantizes LLMs to 8-bit or 4-bit precision using bitsandbytes, achieving 50-75% memory reduction with minimal accuracy loss. It's ideal for running larger models on limited GPU memory or accelerating inference, supporting formats like INT8, NF4, and FP4. The skill integrates with HuggingFace Transformers and enables QLoRA training and 8-bit optimizers.

This skill enables GGUF quantization for efficient model deployment on consumer hardware like CPUs and Apple Silicon. It provides flexible 2-8 bit quantization options without requiring GPU acceleration. Use it when optimizing models for local inference tools or resource-constrained environments.

AWQ is a 4-bit weight quantization technique that uses activation patterns to preserve critical weights, enabling 3x faster inference with minimal accuracy loss. It's ideal for deploying large models (7B-70B) on limited GPU memory and is particularly effective for instruction-tuned and multimodal models. This skill integrates with vLLM and Marlin kernels for optimized deployment.

This skill integrates Weights & Biases for comprehensive ML experiment tracking and MLOps. It automatically logs metrics, visualizes training in real-time, and manages hyperparameter sweeps and model versions. Use it to compare runs, optimize models, and collaborate within team workspaces directly from your development environment.