External Publication

Downloading files

Hugging Face Forums [Unofficial] June 11, 2026

Hmm, without knowing exactly what you are downloading, how you are downloading it, and which browser or tool you are using, nobody can give very precise advice here…

Also, there really are occasional cases where the problem is on Hugging Face’s side, in a recent deep change in the download stack, in a temporary incident, with your ISP, or somewhere along the network path between you and HF. So the first step is to narrow down where the failure is happening. The same kind of issue may also affect other large downloads, not only HF.

Very roughly, for Hugging Face downloads, I would think about it like this:

First, identify the download path

“Downloading from Hugging Face” can mean several different things:

Download path	What it means	First thing to check
Browser download button	Clicking a file in the Hub web UI and letting the browser save it	Browser, OS, Wi-Fi/network, VPN/proxy, file size, where it stops
Browser default downloader	Chrome/Firefox/Edge/Safari’s built-in download UI	Try another browser or a download manager
Download manager	IDM, aria2, browser extension, etc.	Whether it actually resumes or restarts from zero
`hf download` CLI	Hugging Face Hub command line download	`hf --version`, `hf env`, auth, cache, timeout
Python `huggingface_hub`	`hf_hub_download()` or `snapshot_download()`	Python version, `huggingface_hub` version, cache path
`transformers` / `datasets`	Automatic downloads via `from_pretrained()` or `load_dataset()`	Exact function call and package versions
Git / Git LFS / Xet	Repository-style clone/pull/download behavior	Git/LFS/Xet path, file size, repo type
Third-party app	LM Studio, ComfyUI, Ollama-related tooling, etc.	App name/version, then compare with browser or CLI

These paths can fail for different reasons. A browser download problem is not the same diagnostic situation as a Python cache problem, a datasets.load_dataset() problem, or a large-file Xet transfer problem.

The official Hub Python docs describe hf_hub_download() as the main function for downloading a single file and caching it locally, while snapshot_download() downloads an entire repository snapshot. The CLI command hf download uses the same underlying helpers, but gives you more visible logs than a browser download:

Download files from the Hub
Hugging Face Hub CLI guide

If you are using only the browser

Start with the simple checks first.

Please post:

Detail	Why it matters
Browser and version	Browser downloaders behave differently
OS	Windows/macOS/Linux/Android/iOS can matter
Exact repo link	People need to know what file is involved
Exact filename	A model repo may contain many files
File size	500 MB, 5 GB, 50 GB are different cases
Where it stops	Immediately, halfway, near the end, after sleep mode, etc.
Whether it restarts from zero	Important for resume diagnosis
Whether another browser works	Separates browser-specific behavior from network/HF behavior
Whether VPN/proxy is enabled	VPN/proxy/corporate/campus networks can affect large transfers
Whether other large downloads fail	Helps separate “HF-specific” from “general network path”

Simple things worth trying:

Try another browser.
Try a more stable network if possible.
Disable VPN/proxy temporarily if you can.
Avoid letting the machine sleep during a large download.
Try a download manager if you are using the browser’s default downloader.
Try downloading a smaller file from the same repo.
Try downloading another large file from a different site.

This is not about blaming your Wi-Fi. It is just the fastest way to separate local network instability from a site-specific or transfer-stack issue.

Why a download manager may help

The suggestion to try something like IDM is practical.

Download managers sometimes help because HTTP supports “range requests”: instead of requesting the whole file from byte 0 every time, a client can request only a byte range. If the server/path/session supports it, this can allow partial download, multi-part download, or resume behavior.

Useful references:

MDN: HTTP range requests
aria2 manual: --continue

But this is not magic.

A download manager can help only if the server response, URL/session/auth state, and network path allow the tool to resume correctly. Some URLs may expire. Some authenticated or redirected downloads may not resume cleanly. Some large-file backend paths may not behave like a simple static HTTP file.

So the useful report is not just “I tried IDM.” The useful report is:

Browser default downloader:
  result: failed at <where>

Download manager:
  tool: <tool name and version>
  result: resumed / restarted from zero / failed at <where>
  error: <exact error if any>

If you are comfortable with the command line

This is optional. If you only use the browser, say that.

If you can use the terminal, hf download is often a better diagnostic path than a silent browser download because it gives clearer output and uses the official Hub client path.

Install/update:

pip install -U huggingface_hub

Check your environment:

hf --version
hf env
hf auth whoami

Try a single file:

hf download <repo_id> <filename>

For example, if the repo is org/model-name and the file is model.safetensors:

hf download org/model-name model.safetensors

If the file is inside a subfolder, use the path inside the repo:

hf download <repo_id> <path/inside/repo/file>

You can also preview what would be downloaded:

hf download <repo_id> --dry-run

The CLI docs mention --dry-run as a way to list which files would be downloaded and their sizes before actually downloading them:

HF CLI: hf download

For slow connections, the CLI docs also mention increasing the download timeout:

export HF_HUB_DOWNLOAD_TIMEOUT=30
hf download <repo_id> <filename>

Reference:

HF CLI: download timeout
Environment variables: HF_HUB_DOWNLOAD_TIMEOUT

If the problem is with Python, Transformers, or Datasets

Please say the exact code path.

These are not the same:

from huggingface_hub import hf_hub_download



from huggingface_hub import snapshot_download



from transformers import AutoModelForCausalLM
AutoModelForCausalLM.from_pretrained(...)



from datasets import load_dataset
load_dataset(...)

The transformers and datasets libraries may call huggingface_hub under the hood, but the visible symptom can look different from a direct hf download test.

Useful details to post:

Python version:
OS:
huggingface_hub version:
hf_xet version, if installed:
transformers version, if relevant:
datasets version, if relevant:
exact function call:
repo id:
filename or dataset config/split:
public/private/gated:
cache path:
exact error or stall point:

A quick version check:

python - <<'PY'
import platform
print("python:", platform.python_version())

try:
    import huggingface_hub
    print("huggingface_hub:", huggingface_hub.__version__)
except Exception as e:
    print("huggingface_hub unavailable:", repr(e))

try:
    import hf_xet
    print("hf_xet:", getattr(hf_xet, "__version__", "unknown"))
except Exception as e:
    print("hf_xet unavailable:", repr(e))

try:
    import transformers
    print("transformers:", transformers.__version__)
except Exception:
    pass

try:
    import datasets
    print("datasets:", datasets.__version__)
except Exception:
    pass
PY

A note about recent Hugging Face large-file downloads and Xet

For large model or dataset files, Hugging Face downloads are not always just “one simple static file over one simple HTTP connection.”

The Hub stores very large binary files differently from ordinary Git text/code files. Hugging Face’s Xet docs explain that large files are tracked through pointer files and stored remotely, while the repository itself stays small:

Xet: our Storage Backend

The newer Hub client stack also uses hf_xet for Xet-backed transfers. The Xet docs say that as of huggingface_hub 0.32.0, installing/updating huggingface_hub also installs hf_xet, and transformers / datasets use huggingface_hub underneath when their versions are in that range:

Using Xet Storage

This matters because old advice may be stale. For example, the huggingface_hub v1 migration docs say that hf_transfer support was removed, HF_HUB_ENABLE_HF_TRANSFER is ignored, and HF_XET_HIGH_PERFORMANCE is the current high-performance Xet knob:

Migrating to huggingface_hub v1.0: hf_transfer

So if you see older posts saying:

HF_HUB_ENABLE_HF_TRANSFER=1 hf download ...

that may no longer test what you think it tests on current clients.

Optional advanced CLI/Xet tests

Only use these if you are already using CLI/Python downloads.

These are diagnostic switches, not universal fixes.

Try the default path first:

hf download <repo_id> <filename>

Try Xet high-performance mode:

HF_XET_HIGH_PERFORMANCE=1 hf download <repo_id> <filename>

Try disabling Xet to test whether behavior changes:

HF_HUB_DISABLE_XET=1 hf download <repo_id> <filename>

If disabling Xet makes the same file stable, that is useful evidence. It suggests the problem may be related to the Xet path, Xet cache, client version, filesystem, route, CDN path, or local environment.

But do not assume HF_HUB_DISABLE_XET=1 is a permanent fix. For some very large files, the non-Xet HTTP path may not be available or may be limited. There are GitHub reports where a roughly 59 GB file failed through the regular HTTP method with a message saying the file was too large and that hf_xet should be installed:

huggingface_hub issue: HTTP download fails for files >50GB

There are also reports where large downloads behaved differently depending on environment, for example Colab versus local machine:

huggingface_hub issue: Large downloads via HF hub stuck

GitHub issues are not proof that your exact case has the same cause. They are useful examples of why “download failed” needs more detail.

Sometimes it really can be HF or the route

It is also fair to say that the problem is not always local.

Hugging Face’s status page has recorded incidents involving stalled or hung large-file downloads. For example, one April 2026 incident affected Hub file downloads for XET-enabled repositories, especially users routed through certain US Central CDN endpoints:

Hugging Face status page

That does not mean every current download failure is an HF incident. It just means the possibilities include:

local Wi-Fi instability
ISP routing
VPN/proxy/corporate/campus network behavior
regional CDN path
browser downloader behavior
download manager behavior
expired/redirected/authenticated URL behavior
Hugging Face temporary incident
Hub client version issue
Xet transfer path issue
cache or filesystem issue
third-party app behavior

The only practical way forward is to narrow it down.

Quick decision tree

Download failed or restarted
├─ Are you using the browser?
│  ├─ Yes
│  │  ├─ Try another browser
│  │  ├─ Try a stable network / no VPN / no sleep mode
│  │  ├─ Try a download manager
│  │  └─ Post repo, filename, size, browser, OS, failure point
│  └─ No
│
├─ Are you using a third-party app?
│  ├─ Yes
│  │  ├─ Name the app and version
│  │  ├─ Try the same file in the browser
│  │  └─ If possible, compare with `hf download`
│  └─ No
│
├─ Are you using CLI?
│  ├─ Yes
│  │  ├─ Post `hf --version`
│  │  ├─ Post `hf env`
│  │  ├─ Post `hf auth whoami`
│  │  ├─ Try `hf download <repo_id> <filename>`
│  │  └─ If advanced, compare default / Xet high-performance / Xet disabled
│  └─ No
│
└─ Are you using Python / Transformers / Datasets?
   ├─ Post package versions
   ├─ Post exact function call
   ├─ Post cache path if known
   └─ Compare direct `hf download` if possible

Minimal information that would make this diagnosable

A useful follow-up would look like this:

Download method:
  Browser / download manager / hf CLI / Python / transformers / datasets / third-party app

Repo:
  <repo_id>

File:
  <filename>

File size:
  <size>

OS:
  <Windows/macOS/Linux/etc.>

Browser or tool:
  <browser/tool/app name and version>

Network:
  Wi-Fi / wired / VPN / proxy / corporate / campus / cloud / home

Failure point:
  immediately / after <amount> downloaded / near the end / after retry / after sleep

Resume behavior:
  resumes / restarts from zero / unknown

Exact error:
  <copy exact error message if any>

Other tests:
  another browser:
  download manager:
  another network:
  `hf download`:

Without at least some of that information, people can only guess.

Short version

The other replies are pointing at useful first checks: network stability and browser/download-manager behavior. I would start there.

But the broader point is that “Hugging Face download failed” is not one single problem. It can be browser, Wi-Fi, ISP route, CDN path, temporary HF incident, third-party app behavior, Python cache, CLI client version, Xet transfer path, auth, or file size.

So the best next step is not to assume the cause. The best next step is to identify the exact download path and give enough details for others to reproduce or narrow it down.