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huggingface-cli login alternate command is

If huggingface-cli login is not working or you want alternatives, here are all the ways to authenticate with Hugging Face:


✅ 1. New Command (Recommended)

Hugging Face updated the CLI:

hf auth login

✔ This is the replacement for huggingface-cli login


✅ 2. Login inside Python (no CLI needed)

from huggingface_hub import loginlogin("your_huggingface_token")

👉 Get token from: https://huggingface.co/settings/tokens


✅ 3. Use token directly in code

from transformers import AutoTokenizertokenizer = AutoTokenizer.from_pretrained(
"model_name",
token="your_huggingface_token"
)

✅ 4. Set environment variable (best for servers)

Linux / Mac:

export HF_TOKEN=your_token

Windows (CMD):

set HF_TOKEN=your_token

Windows (PowerShell):

$env:HF_TOKEN="your_token"

Then in Python:

from transformers import AutoTokenizertokenizer = AutoTokenizer.from_pretrained("model_name")

✅ 5. Old command (still works sometimes)

huggingface-cli login

🚀 Which one should you use?

Use caseBest method
Local devhf auth login
Script / automationlogin()
Production serverENV variable
Quick testpass token directly

⚠️ Common Issues

  • ❌ “command not found” → install CLI: pip install -U huggingface_hub
  • ❌ Wrong token → regenerate from HF settings
  • ❌ Permission issue → model may be gated

torch.set_float32_matmul_precision(“highest”)

  • “highest”, float32 matrix multiplications use the float32 datatype (24 mantissa bits with 23 bits explicitly stored) for internal computations.
  • “high”, float32 matrix multiplications either use the TensorFloat32 datatype (10 mantissa bits explicitly stored) or treat each float32 number as the sum of two bfloat16 numbers (approximately 16 mantissa bits with 14 bits explicitly stored), if the appropriate fast matrix multiplication algorithms are available. Otherwise float32 matrix multiplications are computed as if the precision is “highest”. See below for more information on the bfloat16 approach.
  • “medium”, float32 matrix multiplications use the bfloat16 datatype (8 mantissa bits with 7 bits explicitly stored) for internal computations, if a fast matrix multiplication algorithm using that datatype internally is available. Otherwise float32 matrix multiplications are computed as if the precision is “high”.