Show HN: Weight-inspect, examine popular ML models formats without the weights (github.com)

The newly launched tool, "weight-inspect," allows users to inspect various model formats—including GGUF, safetensors, and ONNX—without the need to load any weight data. This innovative application extracts key information such as model architecture, tensor shapes, data types, quantization methods, and metadata, enabling users to quickly answer fundamental questions about the models. Its capability to generate a deterministic structural fingerprint allows for verifying whether two models have the same structure, making it a valuable asset for developers and researchers working with machine learning models. Weight-inspect addresses a critical challenge in the AI/ML community, as model files often act as opaque blobs that conceal their internal configurations. By solely reading headers and tensor descriptors, it enhances both accessibility and memory efficiency, enabling inspection of large models without the burden of loading gigabytes of weight data. This facility is not just useful for individual model examination; it also aids in comparing different models, identifying modifications, ensuring model integrity, and detecting structural changes. The tool's focus on structural identity over byte-for-byte equality significantly streamlines model management in machine learning workflows and enhances collaboration within the community.