{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/2"}],"enrichment":{"capability":"Provides a unified format for storing and loading compressed neural network tensors, supporting multiple quantization and sparsity schemes like GPTQ, AWQ, SmoothQuant, INT8, and FP8.","skillfed_tags":["model-compression","quantization","llm-deployment"],"use_cases":["Save a quantized LLM checkpoint after post-training quantization (PTQ) in a format that multiple inference engines can load","Experiment with different quantization schemes (W4A16, W8A8, etc.) on the same model without managing separate storage formats","Build a model deployment pipeline that accepts compressed-tensors checkpoints and applies them uniformly across different quantization methods","Store both weight and activation quantization metadata alongside compressed weights for reproducible model optimization","Handle semi-structured sparsity patterns (e.g., 2:4 sparsity) alongside quantization in a single unified checkpoint"],"what_it_does":"compressed-tensors extends the safetensors format to create a single, standardized way to store compressed neural network models. Rather than each quantization technique (GPTQ, AWQ, SmoothQuant, INT8, FP8, etc.) having its own checkpoint format, this library provides a unified representation that can handle weight-only quantization, activation quantization, KV cache quantization, and both unstructured and semi-structured sparsity patterns.\n\nYou use it to save quantized models to disk and load them back, eliminating the friction of supporting multiple compression formats. The library integrates with Hugging Face models and PyTorch, so you can apply post-training quantization, calibrate on data, compress weights, and save the result in a single consistent format that downstream inference engines can understand.","worth_installing":"Yes. The package is actively maintained, has no known vulnerabilities, installs with low friction, and solves a real problem in the LLM deployment pipeline\u2014unifying the fragmented landscape of quantization formats. If you work with quantized models or need to support multiple compression schemes, it's a practical choice."},"id":"compressed-tensors","links":{"html":"https://skillfed.io/packages/compressed-tensors","md":"https://skillfed.io/packages/compressed-tensors.md","pypi":"https://pypi.org/project/compressed-tensors/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-08","license_spdx":null,"license_treatment":"permissive","name":"compressed-tensors","python_support":"unspecified","summary":"Library for utilization of compressed safetensors of neural network models"},"popularity":{"monthly_downloads":7323086,"position":1753,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.18.0"}