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HuggingFace Model Downloader is a Go-based utility for downloading AI models and datasets from Hugging Face Hub efficiently. It accelerates transfers through parallel connections and simultaneous file downloads. Interrupted transfers can resume automatically, while file verification helps ensure download integrity. Its smart repository analyzer identifies model architectures, quantization formats, memory requirements, and dataset characteristics. An interactive GGUF selector helps users compare model variants before downloading. The utility supports selective downloads, repository revisions, and configurable inclusion or exclusion patterns. Downloaded files can use the standard Hugging Face cache for compatibility with Python machine learning libraries. Additional capabilities include a web interface, proxy configuration, and repository synchronization workflows.
Features
- High-speed parallel downloading with up to 16 connections per file and eight simultaneous transfers
- Automatic interrupted download recovery with SHA-256 verification and progress monitoring
- Interactive GGUF quantization selection with quality ratings, memory estimates, and file size comparisons
- Smart repository analysis for Transformers, Diffusers, LoRA, GPTQ, AWQ, and datasets
- Selective file downloading with branch selection, inclusion filters, and exclusion patterns
- Python-compatible Hugging Face caching with web interface access and authenticated proxy support
Project Samples
