HF-mem: CLI to estimate inference memory requirements for Hugging Face models (github.com)

A new experimental command-line interface (CLI) called hf-mem has been introduced to help users estimate the memory requirements for running inference on Hugging Face models. Developed in Python and designed to be lightweight with a dependency on httpx, hf-mem can efficiently work with a wide range of models available on the Hugging Face Hub, including Transformers, Diffusers, and Sentence Transformers, as well as any models featuring Safetensors-compatible weights. For an optimal experience, it is recommended to use hf-mem in conjunction with the uv library. This tool is significant for the AI/ML community as it addresses a common challenge developers face when deploying machine learning models—understanding the memory implications of specific models before running them. By enabling precise memory estimation, hf-mem can help optimize resource allocation and enhance performance, fostering smoother integration and scalability of AI applications. Users can initiate memory assessments effortlessly using simple commands, making it an accessible resource for researchers and practitioners alike.