compressed-tensors is a production-grade Python library for compressing and quantizing large neural network models, primarily targeting LLMs. It extends the safetensors format to store and manage compressed tensors, supporting a wide range of quantization schemes (INT4/INT8/FP8/NVFP4/MXFP4/MXFP8), sparsity patterns, and rotation-based transforms (Hadamard, random matrix). The library is tightly integrated with the vLLM inference engine and Hugging Face transformers, providing a seamless path from model compression to deployment. Its architecture is modular: a registry pattern for compressors and transforms, a quantization lifecycle with calibration and forward hooks, a flexible offloading subsystem (CPU/disk/distributed) for running models beyond VRAM limits, and checkpoint conversion pipelines for formats like AutoAWQ and ModelOpt NVFP4. While primarily a software library, it also serves as a research platform for novel quantization techniques, with benchmarks and experimental features like MoE expert pruning and KV cache quantization. The codebase is well-structured with extensive tests, CI/CD pipelines, and active maintenance, though it exhibits some coupling to vLLM internals and experimental components that may not be stable.
Netron is a cross-platform neural network model visualizer that supports a wide range of deep learning frameworks (ONNX, TensorFlow, PyTorch, CoreML, etc.). It operates as an Electron desktop application, a web app, and a Python-based CLI/server. The architecture is modular: each model format is handled by a dedicated `ModelFactory` class implementing `match()` and `open()` methods, which parse binary or protobuf/flatbuffers serialized models into a uniform internal graph representation (Model -> Graph -> Node -> Value/Tensor). The application provides interactive graph visualization with SVG rendering, attribute inspection, and export capabilities. While primarily an application, it contains substantial data-processing code for format parsing, tensor decoding, and schema-driven metadata handling. The codebase is well-structured with consistent patterns, though it exhibits some duplication across format parsers and experimental/partial support for minority formats. The Python server (`server.py`) and the core binary parsing libraries (`base.js`, `protobuf.js`, `flatbuffers.js`) are independently reusable.