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

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: For vision tasks, add: For audio tasks, add: Authentication Many models on the Hugging Face Hub require authentication. Set up access: Or set environment variable: Get tokens at: https://huggingface.co/settings/tokens Quick Start Use the Pipeline API for fast inference without manual configuration: 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. When to use : Quick prototyping, simple inference tasks, no custom preprocessing needed. See references/pipelines.md for comprehensive task coverage and optimization. 2. Model Loading and Management Load pre trained models with fine grained control over configuration, device placement, and precision. When to use : Custom model initialization, advanced device management, model inspection. See references/models.md for loading patterns and best practices. 3. Text Generation Generate text with LLMs using various decoding strategies (greedy, beam search, sampling) and control parameters (temperature, top k, top p). When to use : Creative text generation, code generation, conversational AI, text completion. See references/generation.md for generation strategies and parameters. 4. Training and Fine Tuning Fine tune pre trained models on custom datasets using the Trainer API with automatic mixed precision, distributed training, and logging. When to use : Task specific model adaptation, domain adaptation, improving model performance. See references/training.md for training workflows and best practices. 5. Tokenization Convert text to tokens and token IDs for model input, with padding, truncation, and special token handling. When to use : Custom preprocessing pipelines, understanding model inputs, batch processing. See references/tokenizers.md for tokenization details. Common Patterns Pattern 1: Simple Inference For straightforward tasks, use pipelines: Pattern 2: Custom Model Usage For advanced control, load model and tokenizer separately: Pattern 3: Fine Tuning For task adaptation, use Trainer: Reference Documentation For detailed information on specific components: Pipelines : references/pipelines.md All supported tasks and optimization Models : references/models.md Loading, saving, and configuration Generation : references/generation.md Text generation strategies and parameters Training : references/training.md Fine tuning with Trainer API Tokenizers : references/tokenizers.md Tokenization and preprocessing