🧠

Model

HuggingFaceModelDownloader

Name: HuggingFaceModelDownloader
Author: bodaay

by bodaay gh-model--bodaay--huggingfacemodeldownloader

Nexus Index

47.1 Top 100%

S: Semantic 50

A: Authority 0

P: Popularity 66

R: Recency 97

Q: Quality 50

Tech Context

Vital Performance

0 DL / 30D

0.0%

Source →

Audited 47.1 FNI Score

Tiny - Params

- Context

0 Downloads

Commercial APACHE License

Model Information Summary
Entity Passport
Registry ID	gh-model--bodaay--huggingfacemodeldownloader
License	Apache-2.0
Provider	github

📜

Cite this model

Academic & Research Attribution

BibTeX

@misc{gh_model__bodaay__huggingfacemodeldownloader,
  author = {bodaay},
  title = {HuggingFaceModelDownloader Model},
  year = {2026},
  howpublished = {\url{https://github.com/bodaay/huggingfacemodeldownloader}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}

APA Style

bodaay. (2026). HuggingFaceModelDownloader [Model]. Free2AITools. https://github.com/bodaay/huggingfacemodeldownloader

🔬Technical Deep Dive

Full Specifications [+]

Quick Commands

🐙 Git Clone

git clone https://github.com/bodaay/huggingfacemodeldownloader

⚖️ Nexus Index V2.0

Methodology Index Protocol

47.1

TOP 100% SYSTEM IMPACT

Semantic (S) 50

Authority (A) 0

Popularity (P) 66

Recency (R) 97

Quality (Q) 50

💬 Index Insight

FNI V2.0 for HuggingFaceModelDownloader: Semantic (S:50), Authority (A:0), Popularity (P:66), Recency (R:97), Quality (Q:50).

Free2AITools Nexus Index

Verification Authority

HuggingFace API GitHub Metadata Arxiv Citation DB System Audit

Unbiased Data Node Refresh: VFS Live

---

🚀 What's Next?

📊

Find Training Datasets

Discover datasets compatible with this model

📈

Compare Benchmarks

See how this model ranks on standard tests

⚡

Deployment Guide

Understand deployment options

Technical Deep Dive

HuggingFace Downloader

The fastest, smartest way to download models from HuggingFace Hub

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 -i for an interactive picker with quality ratings and RAM estimates:

bash

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:

python

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:

bash

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

Quick Start

Try it first — no installation required:

bash

# 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

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:

bash

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

Now use directly:

bash

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:

bash

hfdownloader analyze

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

Type	What It Shows
GGUF	Interactive picker with quality ratings, RAM estimates, multi-select
Transformers	Architecture, parameters, context length, vocabulary size
Diffusers	Pipeline type, components, variants (fp16, bf16)
LoRA	Base model, rank, alpha, target modules
GPTQ/AWQ	Bits, group size, estimated VRAM
Dataset	Formats, configs, splits, sizes

Multi-Branch Support

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

bash

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:

bash

# 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

bash

# 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

bash

# Maximum parallelism
hfdownloader download owner/repo -c 16 --max-active 8

Flag	Default	Description
`-c, --connections`	8	Connections per file
`--max-active`	3	Concurrent file downloads
`-F, --filters`		Include patterns
`-E, --exclude`		Exclude patterns
`-b, --revision`	main	Branch, 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)

bash

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.

text

~/.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):

bash

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 <dir> produces the exact same result and is kept permanently for script compatibility:

bash

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:

yaml

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

bash

# 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:

bash

hfdownloader serve
# Open http://localhost:8080

Web Dashboard

Cache Browser

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

Cache Browser

All Pages

Page	Features
Analyze	Enter any repo, auto-detect type, see files/sizes, pick GGUF quantizations
Jobs	Real-time WebSocket progress, pause/resume/cancel, download history
Cache	Browse downloaded repos, disk usage stats, search & filter
Mirror	Configure targets, compare differences, push/pull sync
Settings	Token, connections, proxy, verification mode

Server Options

bash

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

bash

# 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:

bash

# 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

Type	URL Format
HTTP	`http://host:port`
HTTPS	`https://host:port`
SOCKS5	`socks5://host:port`
SOCKS5h	`socks5h://host:port` (remote DNS)

Configuration File

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

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

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

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

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:

Platform	Architecture	File
Linux	x86_64	`hfdownloader_linux_amd64_*`
Linux	ARM64	`hfdownloader_linux_arm64_*`
macOS	Apple Silicon	`hfdownloader_darwin_arm64_*`
macOS	Intel	`hfdownloader_darwin_amd64_*`
Windows	x86_64	`hfdownloader_windows_amd64_*.exe`

Build from Source

bash

git clone https://github.com/bodaay/HuggingFaceModelDownloader
cd HuggingFaceModelDownloader
go build -o hfdownloader ./cmd/hfdownloader

Docker

bash

# 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.):

bash

# 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:

bash

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

Or set in config file:

yaml

endpoint: https://hf-mirror.com

CLI Reference

Command	Description
`download`	Download models or datasets (default command)
`analyze`	Analyze repository before downloading
`serve`	Start web server with REST API
`list`	List all downloaded repos
`info`	Show details about a downloaded repo
`rebuild`	Regenerate friendly view from HF cache
`mirror`	Sync cache between locations
`proxy`	Test and show proxy configuration
`config`	Manage configuration
`version`	Show version info

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

What's New in v3.0

Feature	Description
HF Cache Compatibility	Downloads use standard HuggingFace cache structure by default (see Storage Modes)
`--local-dir` flag	One-flag opt-in to flat files at a path of your choice — `huggingface-cli`-style
Dual-Layer Storage	Python-compatible cache + human-readable symlinks
Smart Analyzer	Auto-detect model types, GGUF quality ratings, RAM estimates
Web UI v3	Modern interface with real-time WebSocket progress
Mirror Sync	Push/pull cache between locations
Full Proxy Support	HTTP, SOCKS5, authentication, CIDR bypass
Manifest Tracking	`hfd.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

Variable	Purpose
`HF_TOKEN`	HuggingFace access token
`HF_HOME`	Override `~/.cache/huggingface`
`HTTP_PROXY`	Proxy for HTTP requests
`HTTPS_PROXY`	Proxy for HTTPS requests
`NO_PROXY`	Comma-separated bypass list

License

Apache 2.0 — use freely in personal and commercial projects.

Full CLI Docs • REST API • V3 Features • Issues

🚀 Quick Start

bash

git clone https://github.com/bodaay/HuggingFaceModelDownloader
cd HuggingFaceModelDownloader
go build -o hfdownloader ./cmd/hfdownloader

⚠️ Incomplete Data

Some information about this model is not available. Use with Caution - Verify details from the original source before relying on this data.

View Original Source →

📝 Limitations & Considerations

• Benchmark scores may vary based on evaluation methodology and hardware configuration.
• VRAM requirements are estimates; actual usage depends on quantization and batch size.
• FNI scores are relative rankings and may change as new models are added.
⚠ License Unknown: Verify licensing terms before commercial use.

Social Proof

GitHub Repository

944Stars

Repo Issues

🐙 Data Source: GitHub ↗

🔄 Daily sync (03:00 UTC)

AI Summary: Based on GitHub metadata. Not a recommendation.

📊 FNI Methodology 📚 Knowledge Baseℹ️ Verify with original source

🛡️ Model Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

id: gh-model--bodaay--huggingfacemodeldownloader
slug: bodaay--huggingfacemodeldownloader
source: github
author: bodaay
license: Apache-2.0
tags: golang, huggingface, llm, transformers, go

⚙️ Technical Specs

architecture: null
params billions: null
context length: null
pipeline tag: text-generation

📊 Engagement & Metrics

downloads: 0
stars: 944
forks: 0

Data indexed from public sources. Updated daily.

Welcome to Free2AI Tools!

Smart Search

FNI Score

You're All Set!

Cite this model

🔬Technical Deep Dive

Quick Commands

⚖️ Nexus Index V2.0

💬 Index Insight

Verification Authority

🚀 What's Next?

Find Training Datasets

Compare Benchmarks

Deployment Guide

Technical Deep Dive

HuggingFace Downloader

Why This Tool?

Parallel Downloads

Interactive GGUF Picker

Python Just Works

Works Behind Corporate Firewalls

Quick Start

Smart Analyzer

Multi-Branch Support

Diffusers Component Picker

Download Features

Inline Filter Syntax

Resume & Verify

High-Speed Mode

Storage Modes

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

Mode 2 — Flat files in a directory you choose

Manifest Tracking

Web UI

Cache Browser

All Pages

Server Options

Mirror Sync

Proxy Support

Supported Types

Configuration File

Installation

One-Liner (Recommended)

Download Binary

Build from Source

Docker

Private & Gated Models

China Mirror

CLI Reference

What's New in v3.0

Environment Variables

License

🚀 Quick Start

⚠️ Incomplete Data

📝 Limitations & Considerations

Social Proof

🛡️ Model Transparency Report

🆔 Identity & Source

⚙️ Technical Specs

📊 Engagement & Metrics