Understanding LLM model formats: gguf, gptq, awq, and exl2

The article explains the differences between various LLM model formats, including GGUF, GPTQ, AWQ, and EXL2. GGUF is a binary format for running models with GGML and llama.cpp, introduced in 2023. It supports typed metadata and can include tokenizers and chat templates. GPTQ, developed by researchers at IST Austria and ETH Zurich, uses post-training quantization with calibration data. AWQ, from MIT, focuses on protecting key weights to reduce quantization error. EXL2, a format for ExLlamaV2, uses similar optimization methods as GPTQ and supports multiple bit quantizations. EXL3, its successor, builds on QTIP and adds features like speculative decoding and multimodal support. The article highlights the trade-offs between these formats, emphasizing their use cases and performance differences. Bitsandbytes and MLX are also discussed as tools for running and fine-tuning models on Apple Silicon. GGUF and MLX are noted as strong options for Mac users. The article provides an overview of the technical aspects and practical applications of these formats, aiding developers and researchers in choosing the right model format for their needs.