Use this command to install HuggingFace Model Downloader:
winget install --id=bodaay.hfdownloader -e
The HuggingFace Model Downloader is a utility tool designed to streamline the process of downloading models and datasets from the HuggingFace platform. It simplifies access to machine learning resources by offering efficient and reliable retrieval mechanisms.
Key Features:
Multithreaded LFS File Downloads: Enhances download speeds for large files, making it easier to handle extensive model and dataset files.
SHA256 Checksum Verification: Ensures the integrity of downloaded models, confirming they are unchanged and valid.
Cross-Platform Compatibility: Operates seamlessly across various operating systems, including Linux, macOS, and Windows WSL2.
Configuration File Support: Allows users to set default values for command flags, enhancing customization and ease of use.
Download Resumption: Continues interrupted downloads without starting over, saving time and bandwidth.
HuggingFace Access Token Integration: Supports secure access to restricted models and datasets using tokens.
Audience & Benefit:
Ideal for researchers, data scientists, and developers working with machine learning models. This tool enables efficient retrieval of resources, ensuring reliability and integrity while handling large-scale downloads. Its flexibility and robust features make it a valuable asset in managing machine learning projects effectively.
The HuggingFace Model Downloader can be installed via winget, offering a straightforward setup process to integrate into your workflow seamlessly.
README
HuggingFace Downloader
The fastest, smartest way to download models from HuggingFace Hub
Parallel downloads • Smart GGUF analyzer • Python compatible • Full proxy support
Layer 1 (hub/): Standard HuggingFace cache structure. Python libraries work automatically.
Layer 2 (models/): Human-readable paths with symlinks. Browse your downloads like normal folders.
> Windows Note: The friendly view (Layer 2) requires symlinks, which need Administrator privileges or Developer Mode on Windows. Downloads still work — files are stored in the HuggingFace cache (Layer 1) — but the human-readable symlinks won't be created.
Manifest Tracking
Every download creates hfd.yaml so you know exactly what you have:
git clone https://github.com/bodaay/HuggingFaceModelDownloader
cd HuggingFaceModelDownloader
go build -o hfdownloader ./cmd/hfdownloader
Docker
# Pull from GitHub Container Registry
docker pull ghcr.io/bodaay/huggingfacemodeldownloader:latest
# Or build locally
docker build -t hfdownloader .
# Run (mounts your local HF cache)
docker run --rm -v ~/.cache/huggingface:/home/hfdownloader/.cache/huggingface \
ghcr.io/bodaay/huggingfacemodeldownloader download TheBloke/Mistral-7B-Instruct-v0.2-GGUF
Private & Gated Models
For private repos or gated models (Llama, etc.):
# Set token via environment
export HF_TOKEN=hf_xxxxx
hfdownloader download meta-llama/Llama-2-7b
# Or via flag
hfdownloader download meta-llama/Llama-2-7b -t hf_xxxxx
For gated models, you must first accept the license on the model's HuggingFace page.
China Mirror
Use the HuggingFace mirror for faster downloads in China:
