transformers

name	transformers
description	This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.
license	Apache-2.0 license
compatibility	Some features require an Huggingface token
metadata	{"skill-author":"K-Dense Inc."}
verified	false
lastVerifiedAt	"2026-02-19T05:29:09.098Z"
source	builtin
trust_score	100
provenance_sha	3bc9d56d8a0d4660

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

Install transformers and core dependencies:

uv pip install torch transformers datasets evaluate accelerate

For vision tasks, add:

uv pip install timm pillow

For audio tasks, add:

uv pip install librosa soundfile

Authentication

Many models on the Hugging Face Hub require authentication. Set up access:

from huggingface_hub import login
login()  # Follow prompts to enter token

Or set environment variable:

export HUGGINGFACE_TOKEN="your_token_here"

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

Quick Start

Use the Pipeline API for fast inference without manual configuration:

from transformers import pipeline

# Text generation
generator = pipeline("text-generation", model="gpt2")
result = generator("The future of AI is", max_length=50)

# Text classification
classifier = pipeline("text-classification")
result = classifier("This movie was excellent!")

# Question answering
qa = pipeline("question-answering")
result = qa(question="What is AI?", context="AI is artificial intelligence...")

Core Capabilities

1. Pipelines for Quick Inference

Use for simple, optimized inference across many tasks. Supports text generation, classification, NER, question answering, summarization, translation, image classification, object detection, audio classification, and more.