Introduction to Quantization cooked in 🤗 with 💗🧑‍🍳

Quantization in deep learning is a set of techniques aimed at reducing the precision of model parameters to create smaller models and accelerate training, primarily by using fewer bits to represent numbers. This approach involves transforming model weights from high-precision formats, like FP32, to lower-precision ones, such as int8 or even 4/8-bit representations. While this compression can lead to some performance loss, it significantly decreases model size and speeds up computation. Two main quantization methods are discussed: post-training quantization, which adjusts precision after training, and quantization-aware training, which integrates precision reduction during training to mitigate performance degradation. The blog also introduces GPTQ, a post-training quantization method that reduces memory usage and increases inference speed by minimizing mean squared error, and bitsandbytes, a library for 8-bit and 4-bit quantization that helps deploy large models on smaller hardware. The text further explores tools and libraries within the Hugging Face ecosystem that facilitate these quantization techniques, enabling users to efficiently manage and fine-tune quantized models.