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Install HuggingFace Model Downloader using Winget - wingetCollections
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HuggingFace Model Downloader bodaay

huggingface
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

Go Version License Release Downloads Build Docker

Parallel downloads • Smart GGUF analyzer • Python compatible • Full proxy support

Quick Start • Why This Tool • Smart Analyzer • Web UI • Mirror Sync • Proxy Support

Why This Tool?

Parallel Downloads

Maximize your bandwidth with multiple connections per file and concurrent file downloads:

  • Up to 16 parallel connections per file (chunked download)
  • Up to 8 files downloading simultaneously
  • Automatic resume on interruption

CLI Download Progress

Real-time progress with per-file status, speed, and ETA.

Interactive GGUF Picker

Don't guess which quantization to download. Use for an with quality ratings and RAM estimates:

Versions
3.0.4
3.0.3
3.0.1
3.0.0
2.3.4
2.3.3
2.3.1
2.3.0
2.0.0
1.4.2
Website
github.com
License
Last updated
4/12/2026
Download latest version
-i
interactive picker
hfdownloader analyze -i TheBloke/Mistral-7B-Instruct-v0.2-GGUF

GGUF Analyzer TUI

Interactive mode features:

  • Keyboard navigation - Use ↑↓ to browse, space to toggle selection
  • Quality ratings - Stars (★★★★☆) show relative quality
  • RAM estimates - Know if it'll fit in your VRAM
  • "Recommended" badge - We highlight the best balance (Q4_K_M)
  • Live totals - See combined size as you select
  • One-click download - Press Enter to start, or c to copy command

Without -i, output is text/JSON — perfect for scripts and piping to other tools.

Python Just Works

Downloads go to the standard HuggingFace cache. Python libraries find them automatically:

from transformers import AutoModel
model = AutoModel.from_pretrained("TheBloke/Mistral-7B-Instruct-v0.2-GGUF")  # Just works

Plus, you get human-readable paths at ~/.cache/huggingface/models/ for easy browsing.

Works Behind Corporate Firewalls

Full proxy support including SOCKS5, authentication, and CIDR bypass rules:

hfdownloader download meta-llama/Llama-2-7b --proxy socks5://localhost:1080

Quick Start

Try it first — no installation required:

# Analyze a model with interactive GGUF picker
bash <(curl -sSL https://g.bodaay.io/hfd) analyze -i TheBloke/Mistral-7B-Instruct-v0.2-GGUF

# Download a model
bash <(curl -sSL https://g.bodaay.io/hfd) download TheBloke/Mistral-7B-Instruct-v0.2-GGUF

# Start web UI
bash <(curl -sSL https://g.bodaay.io/hfd) serve

# Start web UI with authentication
bash <(curl -sSL https://g.bodaay.io/hfd) serve --auth-user admin --auth-pass secret

Like it? Install permanently (no sudo):

bash <(curl -sSL https://g.bodaay.io/hfd) install

By default this installs to ~/.local/bin (or ~/bin if that's already on your PATH) so no sudo prompt is needed. Pass an explicit path to override:

# System-wide install (may prompt for sudo)
bash <(curl -sSL https://g.bodaay.io/hfd) install /usr/local/bin

Now use directly:

hfdownloader analyze -i TheBloke/Mistral-7B-Instruct-v0.2-GGUF
hfdownloader download TheBloke/Mistral-7B-Instruct-v0.2-GGUF:q4_k_m
hfdownloader serve
hfdownloader serve --auth-user admin --auth-pass secret   # with authentication

Files go to ~/.cache/huggingface/ — Python libraries find them automatically.

Smart Analyzer

Not sure what's in a repository? Analyze it first:

hfdownloader analyze

For GGUF models, you get an interactive picker (see screenshot above). For other types, the analyzer auto-detects and shows relevant information:

TypeWhat It Shows
GGUFInteractive picker with quality ratings, RAM estimates, multi-select
TransformersArchitecture, parameters, context length, vocabulary size
DiffusersPipeline type, components, variants (fp16, bf16)
LoRABase model, rank, alpha, target modules
GPTQ/AWQBits, group size, estimated VRAM
DatasetFormats, configs, splits, sizes

Multi-Branch Support

Some repos have multiple branches (fp16, onnx, flax). The analyzer lets you pick:

hfdownloader analyze -i CompVis/stable-diffusion-v1-4

Branch Picker

Diffusers Component Picker

For Stable Diffusion models, pick exactly which components you need:

Diffusers Picker

Select unet, vae, text_encoder — skip what you don't need. The command is generated automatically.

Download Features

Inline Filter Syntax

Download specific files without extra flags:

# Download only Q4_K_M quantization
hfdownloader download TheBloke/Mistral-7B-Instruct-v0.2-GGUF:q4_k_m

# Download multiple quantizations
hfdownloader download TheBloke/Mistral-7B-Instruct-v0.2-GGUF:q4_k_m,q5_k_m

# Or use flags
hfdownloader download TheBloke/Mistral-7B-Instruct-v0.2-GGUF -F q4_k_m -E ".md,fp16"

Resume & Verify

# Interrupted? Just run again - automatically resumes
hfdownloader download owner/repo

# Strict verification
hfdownloader download owner/repo --verify sha256

# Preview what would download
hfdownloader download owner/repo --dry-run

High-Speed Mode

# Maximum parallelism
hfdownloader download owner/repo -c 16 --max-active 8
FlagDefaultDescription
-c, --connections8Connections per file
--max-active3Concurrent file downloads
-F, --filtersInclude patterns
-E, --excludeExclude patterns
-b, --revisionmainBranch, tag, or commit

Storage Modes

Two modes are fully supported. Pick whichever fits your workflow — neither is going away.

Mode 1 — HuggingFace cache (default, dual-layer)

hfdownloader download TheBloke/Mistral-7B-Instruct-v0.2-GGUF

Files go into the standard HuggingFace cache so Python libraries (transformers, diffusers, huggingface_hub, llama.cpp's Python bindings, …) find them automatically — nothing to configure.

~/.cache/huggingface/
├── hub/                              # Layer 1: HF cache (Python compatible)
│   └── models--TheBloke--Mistral.../
│       ├── blobs/                    # real files, content-addressed
│       ├── snapshots/a1b2c3d4.../
│       │   └── model.gguf            → symlink to blobs/
│       └── refs/main
│
└── models/                           # Layer 2: human-readable view
    └── TheBloke/
        └── Mistral-7B-GGUF/
            ├── model.gguf            → symlink to hub/.../snapshots/...
            └── hfd.yaml              # download manifest

Layer 1 (hub/): Standard HF cache structure. Python tools just work. Layer 2 (models/): Human-readable paths via symlinks — browse your downloads like normal folders.

> Windows: The friendly view (Layer 2) needs symlinks, which require > Administrator or Developer Mode on Windows. Downloads still succeed — > files land in Layer 1 — but the readable paths in Layer 2 won't be > created. Use Mode 2 below if you want plain files on Windows without > elevated privileges.

Mode 2 — Flat files in a directory you choose

If you want real files at a path of your choice — no cache, no blob hashes, no symlinks — use --local-dir (matching huggingface-cli download --local-dir):

hfdownloader download TheBloke/Mistral-7B-Instruct-v0.2-GGUF \
    --local-dir ./my-model

This is the right mode for:

  • Feeding files directly to llama.cpp, ollama, or any tool that expects a plain directory of weights.
  • Windows users who don't want to enable Developer Mode.
  • Sharing a model over NFS, SMB, or a USB drive — hardlinks and symlinks don't travel well; real files do.
  • Air-gapped transfers and manual backups.

The v2.x-compatible spelling --legacy -o produces the exact same result and is kept permanently for script compatibility:

hfdownloader download TheBloke/Mistral-7B-Instruct-v0.2-GGUF \
    --legacy -o ./my-model

Both spellings are interchangeable; pick whichever reads better in your scripts. They are mutually exclusive on a single command line.

Manifest Tracking

Every download creates hfd.yaml so you know exactly what you have:

repo: TheBloke/Mistral-7B-Instruct-v0.2-GGUF
branch: main
commit: a1b2c3d4...
downloaded_at: 2024-01-15T10:30:00Z
command: hfdownloader download TheBloke/Mistral-7B-Instruct-v0.2-GGUF -F q4_k_m
files:
  - path: mistral-7b.Q4_K_M.gguf
    size: 4368438272

# List everything you've downloaded
hfdownloader list

# Get details about a specific download
hfdownloader info Mistral

Web UI

A modern web interface with real-time progress:

hfdownloader serve
# Open http://localhost:8080

Web Dashboard

Cache Browser

Browse everything you've downloaded with stats, search, and filters:

Cache Browser

All Pages

PageFeatures
AnalyzeEnter any repo, auto-detect type, see files/sizes, pick GGUF quantizations
JobsReal-time WebSocket progress, pause/resume/cancel, download history
CacheBrowse downloaded repos, disk usage stats, search & filter
MirrorConfigure targets, compare differences, push/pull sync
SettingsToken, connections, proxy, verification mode

Server Options

hfdownloader serve \
  --port 3000 \
  --auth-user admin \
  --auth-pass secret \
  -t hf_xxxxx

Mirror Sync

Sync your model cache between machines — home, office, NAS, USB drive.

Mirror Sync

# Add mirror targets
hfdownloader mirror target add office /mnt/nas/hf-models
hfdownloader mirror target add usb /media/usb/hf-cache

# Compare local vs target
hfdownloader mirror diff office

# Push local cache to target
hfdownloader mirror push office

# Pull from target to local
hfdownloader mirror pull office

# Sync specific repos only
hfdownloader mirror push office --filter "Llama,GGUF"

# Verify integrity after sync
hfdownloader mirror push office --verify

Perfect for:

  • Air-gapped environments: Download at home, sync to office
  • Team sharing: Central NAS with all models
  • Backup: Keep a copy on external drive

Proxy Support

Full proxy support for corporate environments:

# HTTP proxy
hfdownloader download owner/repo --proxy http://proxy:8080

# SOCKS5 (e.g., SSH tunnel)
hfdownloader download owner/repo --proxy socks5://localhost:1080

# With authentication
hfdownloader download owner/repo \
  --proxy http://proxy:8080 \
  --proxy-user myuser \
  --proxy-pass mypassword

# Test proxy connectivity before downloading
hfdownloader proxy test --proxy http://proxy:8080

Supported Types

TypeURL Format
HTTPhttp://host:port
HTTPShttps://host:port
SOCKS5socks5://host:port
SOCKS5hsocks5h://host:port (remote DNS)

Configuration File

Save proxy settings in ~/.config/hfdownloader.yaml:

proxy:
  url: http://proxy.corp.com:8080
  username: myuser
  password: mypassword
  no_proxy: localhost,.internal.com,10.0.0.0/8

Installation

One-Liner (Recommended)

bash <(curl -sSL https://g.bodaay.io/hfd) install

That's it. Works on Linux, macOS, and WSL. Installs to ~/.local/bin by default — no sudo required. Pass an explicit path to install somewhere else:

bash <(curl -sSL https://g.bodaay.io/hfd) install /usr/local/bin   # system-wide
bash <(curl -sSL https://g.bodaay.io/hfd) install ~/bin            # custom

Or run without installing:

bash <(curl -sSL https://g.bodaay.io/hfd) download TheBloke/Mistral-7B-Instruct-v0.2-GGUF
bash <(curl -sSL https://g.bodaay.io/hfd) serve   # Web UI

Download Binary

Get from Releases:

PlatformArchitectureFile
Linuxx86_64hfdownloader_linux_amd64_*
LinuxARM64hfdownloader_linux_arm64_*
macOSApple Siliconhfdownloader_darwin_arm64_*
macOSIntelhfdownloader_darwin_amd64_*
Windowsx86_64hfdownloader_windows_amd64_*.exe

Build from Source

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:

hfdownloader download owner/repo --endpoint https://hf-mirror.com

Or set in config file:

endpoint: https://hf-mirror.com

CLI Reference

CommandDescription
downloadDownload models or datasets (default command)
analyzeAnalyze repository before downloading
serveStart web server with REST API
listList all downloaded repos
infoShow details about a downloaded repo
rebuildRegenerate friendly view from HF cache
mirrorSync cache between locations
proxyTest and show proxy configuration
configManage configuration
versionShow version info

Full documentation: docs/CLI.md • docs/API.md • docs/V3_FEATURES.md

What's New in v3.0

FeatureDescription
HF Cache CompatibilityDownloads use standard HuggingFace cache structure by default (see Storage Modes)
--local-dir flagOne-flag opt-in to flat files at a path of your choice — huggingface-cli-style
Dual-Layer StoragePython-compatible cache + human-readable symlinks
Smart AnalyzerAuto-detect model types, GGUF quality ratings, RAM estimates
Web UI v3Modern interface with real-time WebSocket progress
Mirror SyncPush/pull cache between locations
Full Proxy SupportHTTP, SOCKS5, authentication, CIDR bypass
Manifest Trackinghfd.yaml records what/when/how for every download

Both storage modes (HF cache and flat-file --local-dir / --legacy -o) are fully supported and permanent — neither is deprecated. See Storage Modes for when to pick which.

Environment Variables

VariablePurpose
HF_TOKENHuggingFace access token
HF_HOMEOverride ~/.cache/huggingface
HTTP_PROXYProxy for HTTP requests
HTTPS_PROXYProxy for HTTPS requests
NO_PROXYComma-separated bypass list

License

Apache 2.0 — use freely in personal and commercial projects.

Full CLI Docs • REST API • V3 Features • Issues