{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/10"}],"enrichment":{"capability":"Parse, analyze, optimize, and profile ONNX neural network models with support for shape inference, quantization analysis, memory compression, and LLM-specific workloads.","skillfed_tags":["model-optimization","onnx-ecosystem","llm-tools"],"use_cases":["Profile LLM architectures (BERT, GPT, LLaMa, Qwen) to estimate MACs, parameters, and KV cache requirements before deployment","Optimize Stable Diffusion and other diffusion models by analyzing and compressing activation memory across encoder, decoder, and UNet components","Prepare quantized models for edge deployment by analyzing weight compression ratios and memory footprint reduction across INT8/INT4 schemes","Integrate ONNX models into custom inference engines by extracting compute graphs with minimal shape-calculation overhead","Validate model transformations through shape regression testing after applying constant folding or operator fusion optimizations"],"what_it_does":"onnx-tool is a comprehensive toolkit for working with ONNX neural network models. It provides parsing and editing capabilities through an intuitive API, allowing you to load ONNX files, access computation graphs, modify operators and tensor data, and save changes. The package excels at analyzing model structure and performance characteristics through rapid shape inference and detailed profiling that computes MACs, parameter counts, and memory footprint with sparsity awareness.\n\nBeyond analysis, the toolkit offers optimization and transformation features including constant folding, operator fusion, weight quantization (FP16, INT8/INT4 with multiple schemes), and activation memory compression. It includes specialized support for large language models with KV cache analysis, diffusion models, and computer vision architectures. The compute graph engine removes shape-calculation overhead for efficient inference integration, making it useful for both model development and deployment preparation.","worth_installing":"Yes. The package fills a genuine need for ONNX model analysis and optimization with low install friction, active maintenance, permissive licensing, and no known vulnerabilities. It is most valuable for developers working with LLMs, diffusion models, or deploying neural networks to resource-constrained environments where profiling and compression are critical."},"id":"onnx-tool","links":{"html":"https://skillfed.io/packages/onnx-tool","md":"https://skillfed.io/packages/onnx-tool.md","pypi":"https://pypi.org/project/onnx-tool/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-04-19","license_spdx":null,"license_treatment":"permissive","name":"onnx-tool","python_support":"unspecified","summary":"A tool for parsing, editing, optimizing, and profiling ONNX models."},"popularity":{"monthly_downloads":110862,"position":12439,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.0.1"}