{"library":"optimum-quanto","type":"library","category":null,"description":"Optimum Quanto is a PyTorch quantization backend for Hugging Face Optimum, enabling efficient training and inference of large language models (LLMs) and other neural networks with reduced precision (e.g., 8-bit integers or 8-bit floats). It focuses on model optimization for hardware acceleration by integrating with PyTorch's native quantization functionalities. The current version is 0.2.7. As a rapidly evolving library deeply integrated with the Hugging Face ecosystem and PyTorch's quantization efforts, its release cadence is generally frequent, often tied to major Optimum or PyTorch updates.","language":"python","status":"active","version":"0.2.7","tags":["quantization","pytorch","huggingface","llm","optimization","deep-learning","model-compression"],"install":[{"cmd":"pip install optimum-quanto","imports":["from optimum.quanto import quantize","from optimum.quanto import freeze","from optimum.quanto import set_qtype"]}],"homepage":null,"github":"https://github.com/huggingface/optimum-quanto","docs":null,"changelog":null,"pypi":"https://pypi.org/project/optimum-quanto/","npm":null,"openapi_spec":null,"status_page":null,"smithery":null,"compatibility":{"summary":{"python_range":"3.10–3.9","success_rate":40,"avg_install_s":69.8,"avg_import_s":6.99,"wheel_type":"wheel"},"url":"https://checklist.day/v1/registry/optimum-quanto/compatibility"},"provenance":{"verified_status":"passing","verified_at":"Sun Jun 28","last_verified":"Sun Jun 28","next_check":"Tue Jul 28","install_tag":null}}