transformers

Transformers

Overview

The Hugging Face Transformers library provides access to thousands of pre-trained models for tasks across NLP, computer vision, audio, and multimodal domains. Use this skill to load models, perform inference, and fine-tune on custom data.

Installation

Tested against transformers 5.12.0 (current PyPI release; June 2026). Requires Python 3.10+; the torch extra currently requires PyTorch 2.4+.

uv pip install "transformers[torch]==5.12.0" huggingface_hub==1.19.0 datasets==5.0.0 evaluate==0.4.6 accelerate==1.14.0

For vision tasks, add:

uv pip install timm==1.0.27 pillow==12.2.0

For audio tasks, add:

uv pip install librosa==0.11.0 soundfile==0.14.0

These pins are for reproducible examples. For exploratory work, loosen them only after checking the Transformers and Hub release notes for API changes.

Check your version:

import transformers
print(transformers.__version__)

Authentication

Many models on the Hugging Face Hub are gated or private. Authenticate before loading them.

Recommended: CLI login (stores token in ~/.cache/huggingface/token):

hf auth login

Python:

from huggingface_hub import login
login()  # Interactive prompt; do not hardcode tokens in scripts

Servers / CI: set HF_TOKEN in the environment (never commit tokens to git or shell profiles):

export HF_TOKEN="..."  # Read token from a secret manager, not source code

Get tokens at: https://huggingface.co/settings/tokens

Security: Never paste tokens into notebooks, repos, or shared configs. Prefer hf auth login over exporting tokens in .bashrc or .zshrc.

Use the narrowest token scope that works: read for private or gated model downloads, write only for uploads. If a long-running environment should not send the stored token on every Hub request, set HF_HUB_DISABLE_IMPLICIT_TOKEN=1 and pass a token only where authentication is required.