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 5.9.x (stable; May 2026). Requires Python 3.10+ and PyTorch 2.4+.

uv pip install "transformers[torch]>=5.9" huggingface_hub datasets evaluate accelerate

For vision tasks, add:

uv pip install timm pillow

For audio tasks, add:

uv pip install librosa soundfile

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.

Transformers v5

Transformers v5 is PyTorch-only (TensorFlow and JAX backends were removed). For upgrades from v4, see the v5 migration guide. New projects should pair transformers 5.x with huggingface_hub 1.x.

Gated or custom architectures: accept the model license on the Hub, then load with trust_remote_code=True only when the model card requires custom code you have reviewed.

Cache location: set HF_HOME for a writable cache root (Hub files default under $HF_HOME/hub).