The Error
You try to load a Llama 3 model from Hugging Face — via transformers, huggingface_hub, vLLM, or anything else that wraps the HF API — and hit this immediately:
huggingface_hub.utils._errors.GatedRepoError: 401 Client Error. Cannot access gated repo for url https://huggingface.co/meta-llama/Llama-3-8B/resolve/main/config.json
Happens on Linux, macOS, and Windows alike. The error fires the moment the library tries to pull the first file from the model repo.
What's Actually Happening
Meta's Llama models — along with Mistral, Gemma, Falcon, and dozens of others — live in gated repositories on HuggingFace. Getting past the gate requires three conditions, all at once:
- An active Hugging Face account
- You've visited the model page and accepted its license
- Your local environment is authenticated with a valid HF token from that account
A 401 means HuggingFace's servers couldn't verify you. Either no token was sent, the token is invalid, or — the most common culprit — your account never accepted the gating terms.
Step 1: Accept the Model License (Don't Skip This)
Most people assume having a token is enough. It isn't. You need to explicitly accept the model's access conditions through the HuggingFace website — there's no CLI shortcut for this part.
- Go to https://huggingface.co/meta-llama/Llama-3-8B
- Click "Request access" or "Acknowledge license" (the label varies by model)
- Fill in any required fields and submit
- Wait for the approval email — Meta typically responds within minutes to a few hours
You'll get a confirmation email once approved. Only after that will your token unlock this specific repo.
Step 2: Get a Read Token
Head to https://huggingface.co/settings/tokens and create a token with at least Read permission. Tokens look like hf_aBcDeFgHiJkLmNoPqRsTuVwXyZ1234567 — about 37 characters starting with hf_. Copy it now; you won't see it again after leaving the page.
Quick Fix: Authenticate via CLI
Fastest way to get unblocked:
pip install -U huggingface_hub
huggingface-cli login
Paste your token when prompted. It saves to ~/.cache/huggingface/token and every subsequent HF call picks it up automatically. Retry your script — the download should start.
Permanent Fix: Pass the Token in Code
CLI login doesn't survive Docker containers, CI pipelines, or fresh VMs. For automated environments, pass the token explicitly:
from transformers import AutoModelForCausalLM, AutoTokenizer
import os
hf_token = os.environ.get("HF_TOKEN")
model = AutoModelForCausalLM.from_pretrained(
"meta-llama/Llama-3-8B",
token=hf_token
)
tokenizer = AutoTokenizer.from_pretrained(
"meta-llama/Llama-3-8B",
token=hf_token
)
Or with huggingface_hub directly:
from huggingface_hub import snapshot_download
import os
snapshot_download(
repo_id="meta-llama/Llama-3-8B",
token=os.environ.get("HF_TOKEN")
)
Set the environment variable before running:
# Linux / macOS
export HF_TOKEN="hf_your_token_here"
python your_script.py
# Windows (PowerShell)
$env:HF_TOKEN = "hf_your_token_here"
python your_script.py
# Docker
docker run -e HF_TOKEN="hf_your_token_here" your-image python your_script.py
Working in a notebook? Call login() directly — just don't commit the token to version control:
from huggingface_hub import login
login(token="hf_your_token_here")
Verify the Fix
Before retrying your script, confirm HuggingFace actually sees your credentials:
huggingface-cli whoami
You should see your username. "Not logged in" means the token didn't register.
Then check that your account has actual access to the gated model:
from huggingface_hub import model_info
info = model_info("meta-llama/Llama-3-8B", token="hf_your_token_here")
print(info.id) # Should print: meta-llama/Llama-3-8B
Still getting a 403 instead of 401? That means license acceptance is pending or hasn't propagated yet. Wait a few minutes and retry.
Common Mistakes
- Wrong token type: Fine-grained tokens with limited scopes may not cover repo reads. Use a standard Read token to avoid this.
- Mismatched account: The account that accepted the license must match the one your token belongs to. If you have multiple HF accounts, double-check which one you used.
- Stale cached token: Rotated your token? The old one might still be sitting in cache. Clear it with
rm ~/.cache/huggingface/token, then log in again. - Approval still in review: Meta's turnaround is usually quick — but not instant. If you just submitted the form, give it 15–30 minutes before assuming something is broken.
Tips
After a successful download, the model lands in ~/.cache/huggingface/hub/ — Llama 3 8B takes up roughly 16 GB in float16, so make sure you have the disk space before kicking off the download. Need to inspect the model's config.json?
The JSON Formatter at ToolCraft is handy for spotting structural issues — runs entirely in the browser, nothing uploaded. Dealing with YAML-based model configs instead? The YAML ↔ JSON Converter on the same site beats writing a one-off Python script just to check a single config value.