huggingface-cli Is Gone, and the Answer to the SSL Error Is Elsewhere

Continuing the thread from the Ollama post, I was looking at how to put a local LLM behind some tooling. Once you’ve picked the engine that runs it, the next question is where the models come from and how you get them — which is how I ended up saving a write-up covering three ways to download a Hugging Face model as a whole folder. The title attaches a condition: “for when the API download won’t work and you get SSL errors.”

It’s from April 2025. Checking it, the command two of the three methods rely on no longer exists. And on the condition in the title — the SSL error situation — the method it recommends is right, but the reason it gives isn’t the real one.

Table of contents

Two of the three methods don’t run
Recommending git for SSL errors is right, for a different reason
Fixing the Python side
The example model name doesn’t download
“Has / has no security issues” is never explained
What local_dir does
What still holds
Wrapping up
References

Two of the three methods don’t run

The note attached to method 1:

⚠️ If the model requires authentication, log in to huggingface, get a token and set it as an environment variable, or run huggingface-cli login!

Method 3 is that CLI outright.

huggingface-cli login
huggingface-cli repo clone mistralai/Mistral-7B-Instruct --type model

From the huggingface_hub v1.0 migration document:

The deprecated huggingface-cli has been removed, hf (introduced in v0.34) replaces it with a clearer resource-action CLI.

Removed. hf arrived in v0.34, both ran side by side for a while, and v1.0 dropped the old one. Translated:

The article	Now
`huggingface-cli login`	`hf auth login`
`huggingface-cli whoami`	`hf auth whoami`
`huggingface-cli repo clone <id> --type model`	`hf download <id> --local-dir <path>`

That last row isn’t just a rename. There is no repo clone subcommand in hf today. repo is for creating and managing repositories; downloading is download’s job. What the article describes as “internally uses git-lfs to pull all the files the same way” has been replaced by hf download, which doesn’t go through git-lfs at all.

hf auth login
hf download mistralai/Mistral-7B-Instruct-v0.3 --local-dir ./mistral

snapshot_download() itself is unchanged. The Python function survived and only the CLI split, so method 1 still holds and method 3 has to be rewritten from scratch.

Recommending git for SSL errors is right, for a different reason

The situation the title names is an SSL error. But the labels the article attaches don’t line up.

Method 1, snapshot_download() — “recommended! (has security issues)”
Method 2, Git + Git LFS — “(no security issues)”, “((fallback when SSL can’t be bypassed))”

All three methods connect to https://huggingface.co. In an environment where TLS certificate verification fails, git clone should hit the same wall. Yet in practice the git route sometimes works, and that’s presumably where the article’s observation came from.

The reason isn’t a bypass — it’s that they consult different CA lists. From the requests documentation:

Requests uses certificates from the package certifi. This allows for users to update their trusted certificates without changing the version of Requests.

huggingface_hub does HTTP from Python, and the requests underneath it reads a CA list bundled in a package called certifi, not the operating system’s trust store. In an environment with a corporate inspection appliance in the middle, that corporate CA may be installed in the Windows certificate store and still absent from certifi. That’s why only Python fails while fetching the same address.

Method	Who does the HTTP	Trust list
`snapshot_download()` / `hf`	Python `requests`	the `certifi` bundle
`git clone`	git	git’s backend’s store

Not “a bypass” but “switching to a client that reads a different store.” So method 2 working isn’t luck — and it also isn’t a fix.

Fixing the Python side

The requests docs put the method in the very next paragraph.

This list of trusted CAs can also be specified through the REQUESTS_CA_BUNDLE environment variable. If REQUESTS_CA_BUNDLE is not set, CURL_CA_BUNDLE will be used as fallback.

Point it at the CA certificate file your organization issued.

# Windows (PowerShell)
$env:REQUESTS_CA_BUNDLE = "C:\certs\corp-ca.pem"

# macOS / Linux
export REQUESTS_CA_BUNDLE=/etc/ssl/certs/corp-ca.pem

If you want to know where the current bundle lives:

from requests.utils import DEFAULT_CA_BUNDLE_PATH
print(DEFAULT_CA_BUNDLE_PATH)

I’m deliberately not writing down how to disable verification. An SSL error usually means something in the middle is opening your traffic, and switching verification off is a declaration that you trust that something unconditionally. Registering the certificate takes more steps, but it’s a different kind of act.

The example model name doesn’t download

All three of the article’s methods use the same id.

snapshot_download(
    repo_id="mistralai/Mistral-7B-Instruct",
    local_dir="C:/Users/myname/Downloads/mistral_model"
)

mistralai/Mistral-7B-Instruct doesn’t download as written. Opening it anonymously returns a 401. Mistral’s instruct models carry version suffixes like -v0.1, -v0.2, -v0.3, and they’re gated repositories requiring you to accept terms.

The article’s own “extra tips” section covers what to do on a 403 — token login for private or gated repos, or clicking the access request button on the web page — and its own example is exactly that case. Anyone following along hits the wall before reaching the tip. Using a repository without conditions, like gpt2, would have kept the flow intact.

“Has / has no security issues” is never explained

Back to the labels I set aside. The explanation isn’t anywhere in the article. And it’s hard to make one hold. Methods 1 and 2 fetch the same files from the same repository. Taking a different route doesn’t change the files.

The real security question isn’t in how you download but in the moment you open what you downloaded — and that applies equally to both. Two things.

Weight format. The safetensors docs state their reason for existing:

a new simple format for storing tensors safely (as opposed to pickle) and that is still fast (zero-copy)

.bin-family files are Python pickles, which can run code during deserialization. If .safetensors is available, take that one.

Custom code execution. From transformers on trust_remote_code:

This option should only be set to True for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.

The article’s third step is AutoModelForCausalLM.from_pretrained(model_path), and depending on the model you’ll be told to enable that option. The moment you do, the repository’s code runs on your machine. If there’s a place to write “security issue,” it’s here.

What `local_dir` does

The article’s example uses local_dir, and knowing how it differs from the default saves confusion later. From the current docs:

By default, we recommend using the cache system to download files from the Hub. … However, if you need to download files to a specific folder, you can pass a local_dir parameter. This is useful to get a workflow closer to what the git command offers.

By default it lands in the cache (under HF_HOME); pass local_dir and it unpacks into that folder with the original structure. That’s what let the article place it beside git clone. One behavior comes along with it:

A .cache/huggingface/ folder is created at the root of your local directory containing metadata about the downloaded files. This prevents re-downloading files if they’re already up-to-date.

A .cache/huggingface/ appears inside the folder you specified. Not knowing that is puzzling the first time you move or archive a model folder.

For the record, v1.0 removed three parameters: local_dir_use_symlinks, resume_download and force_filename. If you see those in old code or old write-ups, they need deleting now.

What still holds

The flow of downloading a whole folder and using it as a local path is intact, and the article’s strength is carrying it all the way through. Plenty of write-ups stop at the download; this one goes on to hand the folder to from_pretrained.

from transformers import AutoTokenizer, AutoModelForCausalLM

model_path = "C:/Users/myname/Mistral-7B-Instruct"

tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path)

The local-versus-API comparison table is sound in shape too. Local loads the whole model and uses memory; the API barely touches client memory; running a 7–8B model locally wants 12–16GB of VRAM or more — still a usable rough guide. That said, Inference pricing has been restructured several times since, so “free up to a certain usage” needs rechecking against today.

Running git lfs install first and working from Git Bash are still good advice.

Wrapping up

huggingface-cli was removed in huggingface_hub v1.0. It’s hf auth login and hf download now, and there is no repo clone subcommand. The article’s method 3 needs rewriting entirely.
snapshot_download() itself is unchanged. The Python function stayed; only the CLI split.
Recommending git for SSL errors works, but for a different reason. Not a bypass: Python’s requests reads the certifi bundle, not the OS store.
The Python-side answer is REQUESTS_CA_BUNDLE. Point it at the corporate CA file. Disabling verification is a different kind of choice.
The example mistralai/Mistral-7B-Instruct doesn’t download. No version suffix, and it’s a gated repository — the exact case the article’s own 403 tip describes.
“Has / has no security issues” is unexplained and hard to justify. Same files either way. The real risks are pickle-format weights and trust_remote_code, neither of which depends on how you downloaded.
Passing local_dir unpacks into that folder instead of the cache and creates a .cache/huggingface/ inside it. local_dir_use_symlinks and two other parameters were removed in v1.0.

In a write-up about how to download things, the first thing to spoil was the CLI command. The library function lasted a year and a half; the command-line tool changed its name.

And the condition in the title turned out to be the most interesting part. Finding a way that works and knowing why it works are different things. The article did the first, which is why it was worth writing down. But record the reason as “a bypass” and there’s nothing you can do next. One name — certifi — gives you somewhere to fix.

From the position of someone wiring up a local LLM, here’s what’s left. Picking the engine and fetching the model are separate problems, and where you get stuck on the fetching side is usually the route, not the model. Certificates, access approval, a renamed command — none of them are about the model, and all three catch you here.