Few things break a development flow faster than a 401 Unauthorized error when you know your credentials are correct. If you are attempting to load Meta’s Llama 3 (or Llama 3.2) using the transformers library and receiving a "Repository Not Found" or 401 error, you are likely encountering a specific friction point regarding gated model access.
This is not a generic connectivity issue. It is a handshake failure between your local environment's authentication headers and the specific access requirements of the Meta Llama repositories on the Hugging Face Hub.
Here is the root cause analysis and the definitive, production-grade solution to get your inference pipeline running.
The Root Cause: Gated Repositories and API Obfuscation
To resolve this, we must understand why the error message is often misleading.
When you request meta-llama/Meta-Llama-3-8B, the Hugging Face Hub API checks two things:
- Authentication: Is the request accompanied by a valid User Access Token?
- Authorization: Has the user associated with that token explicitly agreed to the license terms for this specific model?
If condition #1 fails, you get a 401. However, if condition #1 passes but condition #2 fails, the API often returns a 404 Repository Not Found or a generic 401.
This is a security design choice known as resource obfuscation. By returning a 404/401 instead of a specific "License Not Signed" error, the system prevents unauthorized users from mapping out the existence of private or gated datasets. Consequently, your Python script behaves as if the model ID is a typo, even when the model exists.
Phase 1: The Administrative Fix (Mandatory)
Before touching a single line of Python, you must clear the legal gate. Meta requires explicit consent to their Acceptable Use Policy for Llama 3 models.
- Navigate to the Model Card: Go to the official Meta Llama 3 repository (or the specific variant you are using).
- Log In: Ensure you are logged into your Hugging Face account.
- Accept the License: You will see a form asking for your contact details and agreement to the license. Fill this out and click "Agree."
- Wait for Approval: For Llama 3, approval is usually instantaneous, but it can take up to an hour in edge cases. Refresh the page; if you see the model files, you are approved.
Note: If you are using Llama-3.2, you must repeat this process for that specific model family. Acceptance for Llama 2 or Llama 3.0 does not automatically cascade to Llama 3.2.
Phase 2: The Technical Implementation
Once approved, you need to securely pass your credentials. Hardcoding tokens into scripts is a security vulnerability. We will use the huggingface_hub library combined with environment variables for a robust solution.
Step 1: Generate a Fine-Grained Token
- Go to your Hugging Face Settings > Access Tokens.
- Create a new token.
- Type: specific "Read" permissions are safer than "Write".
- Copy the token (starts with
hf_...).
Step 2: Environment Configuration
In your terminal, export the token. This keeps it out of your source control.
# Mac/Linux
export HF_TOKEN="hf_your_actual_token_here"
# Windows PowerShell
$env:HF_TOKEN = "hf_your_actual_token_here"
Step 3: The Production Code
The following Python script implements robust authentication handling. It explicitly attempts to log in via code if the environment variable is present, ensuring the transformers library has the necessary headers.
Prerequisites:
pip install --upgrade transformers huggingface_hub torch accelerator
The Python Solution:
import os
import sys
from huggingface_hub import login
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
def load_llama_model(model_id: str):
"""
Securely loads a gated Llama 3 model using environment variables for auth.
"""
# 1. Secure Authentication Retrieval
hf_token = os.getenv("HF_TOKEN")
if not hf_token:
print("Error: HF_TOKEN environment variable not set.")
print("Please export your Hugging Face token before running.")
sys.exit(1)
# 2. Explicit Login (Ensures local cache is updated)
# This writes the token to ~/.cache/huggingface/token
try:
login(token=hf_token)
print(f"Successfully authenticated to Hugging Face Hub.")
except Exception as e:
print(f"Authentication failed: {e}")
sys.exit(1)
print(f"Attempting to load: {model_id}...")
try:
# 3. Model Loading with Explicit Token Handling
# Note: 'token=True' forces transformers to look for the auth credential
tokenizer = AutoTokenizer.from_pretrained(
model_id,
token=True
)
model = AutoModelForCausalLM.from_pretrained(
model_id,
token=True,
torch_dtype=torch.bfloat16, # Recommended for Llama 3
device_map="auto"
)
print("Model loaded successfully!")
return tokenizer, model
except OSError as e:
if "401" in str(e):
print("\nCRITICAL ERROR: 401 Unauthorized")
print("Checklist:")
print("1. Have you accepted the license on the Hugging Face model page?")
print("2. Is your token valid?")
print(f"3. Do you have access to {model_id}?")
elif "404" in str(e):
print(f"\nError: Repository {model_id} not found. Check spelling or gated access.")
else:
print(f"An unexpected error occurred: {e}")
sys.exit(1)
if __name__ == "__main__":
# Example: Loading Llama 3.2 1B (Lightweight version)
TARGET_MODEL = "meta-llama/Llama-3.2-1B"
tokenizer, model = load_llama_model(TARGET_MODEL)
# Simple inference sanity check
inputs = tokenizer("Hello, I am a software engineer.", return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Deep Dive: Why This Code Works
The login() Function
While transformers can technically read tokens from environment variables automatically, using huggingface_hub.login(token=hf_token) performs a validation handshake against the API immediately. This fails fast if the token is invalid, rather than waiting for the heavy model download to initiate and fail halfway through.
The token=True Parameter
In older versions of transformers, you might see use_auth_token=True. This is deprecated. The modern standard is token=True (which uses the locally cached token) or passing the string directly (not recommended for security). By explicitly setting token=True, we override any anonymous public access attempts that default libraries might attempt.
torch.bfloat16
Llama 3 is trained in bfloat16. Loading it in standard float32 doubles memory usage, and loading in float16 can cause numerical instability. Explicitly defining the dtype ensures the model loads exactly as Meta intended.
Common Pitfalls and Edge Cases
1. The Jupyter/Colab Context
If you are running this in a Jupyter Notebook or Google Colab, environment variables set in the terminal before launching the notebook server might not persist depending on how the kernel was started.
Fix: Use the interactive login widget provided by Hugging Face within the notebook cell:
from huggingface_hub import notebook_login
notebook_login()
2. Git Credential Helper Conflicts
If you have previously used git to clone Hugging Face repos, your machine might have an old token cached in the git credential manager. This often overrides the Python environment.
Fix: Force a logout and re-login via CLI:
huggingface-cli logout
huggingface-cli login
3. Fine-Grained Permissions
If you are using the new "Fine-Grained" access tokens (beta), ensure you have selected permissions for "Read access to contents of all public gated repositories". Standard "Read" tokens cover this by default, but scoped tokens often miss the "Gated" permission checkbox.
Conclusion
The "401 Repository Not Found" error for Llama 3 is rarely a code issue—it is almost always an authorization flow issue. By accepting the license on the Hub, utilizing environment variables for security, and handling authentication explicitly in your Python code, you ensure a stable pipeline for your AI applications.
Once authenticated, you are ready to move on to the actual work: quantization, fine-tuning, or RAG implementation.