transformerslisted

# 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: ```bash uv pip install torch transformers datasets evaluate accelerate ``` For vision tasks, add: ```bash uv pip install timm pillow ``` For audio tasks, add: ```bash uv pip install librosa soundfile ``` ## Authentication Many models on the Hugging Face Hub require authentication. Set up access: ```python from huggingface_hub import login login() # Follow prompts to enter token ``` Or set environment variable: ```bash 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: ```python 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. S