#model quantization

Half-Quadratic Quantization (HQQ) offers a method for fast and detailed model quantization without requiring calibration data, suitable for large-scale models. It supports quantization from 8 to 1 bit and is compatible with optimized kernels and peft training. The method enhances both inference and training speed, particularly with torch.compile integration. Configurable settings such as nbits and group size allow for achieving optimal model quality. The tool is user-friendly, needing only minimal layer replacements and allowing for tailored configurations. Available via pip, it seamlessly integrates with HuggingFace transformers for effective quantization.

Mistral.rs facilitates fast LLM inference with advanced quantization and optimized processing for various devices, including Apple silicon, CPUs, and CUDA. It allows integration through Rust and Python APIs and supports an OpenAI-compatible server for multi-platform deployment. Features include efficient MoE models, direct quantization, and sampling techniques to improve machine learning workflows. Access prequantized models and employ prompt chunking and adaptable LoRA adapters for enhanced efficiency.

Discover Jlama, a modern Java inference engine enabling the integration of Llama, BERT, and GPT-2 models. Features include paged attention, tool calling, distributed inference, and compatibility with the new Vector API for enhanced processing speeds. Easily integrate these capabilities into Java projects using Langchain4j while benefiting from extensive documentation and community support.

AIMET is designed for enhanced neural network performance through state-of-the-art quantization and compression methods, while facilitating easy integration into PyTorch and TensorFlow via user-friendly APIs. Discover optimized models in the Model Zoo and maintain precision with techniques like Data-Free Quantization.