Download and manage model files

App version: 1.0.1iPad · iPhoneContent checked: 2026-09-21Screenshot UI: EnglishStandard / Pro

What you will do

Verify a small Hugging Face download before trying a large model.

Before you start

• An active Standard or Pro subscription, or an eligible trial.
• A working internet connection for the service or download used in this tutorial.
• A Hugging Face account and a token with the required resource permissions, if the resource requires authentication.

Download the tutorial workspace

Where to start

Notebook → huggingface_hub

Follow the steps

1. 1Check access requirements firstThis exercise uses your saved token to verify an authenticated download. Public resources may also allow anonymous access; private or gated resources need account permissions as well as a valid token.
2. 2Download a small configuration fileStart with a configuration file instead of full weights. Run the example in a notebook cell.
   Copy code

   import json
   try:
       from huggingface_hub import hf_hub_download
       path = hf_hub_download("google-bert/bert-base-uncased", "config.json",
                              token=True, force_download=True)
       with open(path) as config_file:
           config = json.load(config_file)
       print("Configuration downloaded:", config.get("model_type") == "bert")
   except Exception as error:
       status = getattr(getattr(error, "response", None), "status_code", None)
       print("Download error:", type(error).__name__, "HTTP:", status)
   

3. 3Find the cache and downloaded filesVibeit places its default Hugging Face cache in the Models directory. A custom HF_HOME environment value can change that location.
4. 4Check compatibility before loading weightsA successful download does not guarantee the model can run. Check the required package, model format, available memory and device backend.

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1. Check access requirements first — This exercise uses your saved token to verify an authenticated download. Public resources may also allow anonymous access; private or gated resources need account permissions as well as a valid token.

1

2. Download a small configuration file — Start with a configuration file instead of full weights. Run the example in a notebook cell.

import json
try:
    from huggingface_hub import hf_hub_download
    path = hf_hub_download("google-bert/bert-base-uncased", "config.json",
                           token=True, force_download=True)
    with open(path) as config_file:
        config = json.load(config_file)
    print("Configuration downloaded:", config.get("model_type") == "bert")
except Exception as error:
    status = getattr(getattr(error, "response", None), "status_code", None)
    print("Download error:", type(error).__name__, "HTTP:", status)

2

3. Find the cache and downloaded files — Vibeit places its default Hugging Face cache in the Models directory. A custom HF_HOME environment value can change that location.

3

4. Check compatibility before loading weights — A successful download does not guarantee the model can run. Check the required package, model format, available memory and device backend.

4

Check the result

Configuration downloaded: True confirms that the small configuration file was downloaded and parsed. It does not verify model inference or access to other models.

If it does not work

• If a newly saved token or environment value is not visible to Python, restart the kernel.
• For 401/403 responses, check the token and the resource’s account permissions.
• If a file downloads but does not load, check its format, required packages and available memory.

Next steps

• Configure a Hugging Face token
• Find and install compatible Python packages

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