SKILL

transformers

处理自然语言、计算机视觉、音频或多模态任务的预训练 transformer 模型时使用。用于文本生成、分类、问答、翻译、摘要、图像分类、目标检测、语音识别,以及在自定义数据集上微调模型。

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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.

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原文涵盖的主题

  • Transformers
  • Overview
  • Installation
  • Authentication
  • Quick Start
  • Core Capabilities
  • 1. Pipelines for Quick Inference
  • 2. Model Loading and Management

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. Install transformers and core dependencies: For vision tasks, add: For audio tasks, add: Many models on the Hugging Face Hub require authentication. Set up access: Or set environment variable: Get tokens at: https://huggingface.co/settings/tokens Use the Pipeline API for fast inference without manual configuration: 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. 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.

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来源仓库
davila7/claude-code-templates
源文件
cli-tool/components/skills/scientific/transformers/SKILL.md
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21be197f100a12b8d0dd9a211cc244c8179f84d3
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原作者:davila7/claude-code-templates · 原作者仓库元数据。文档核查:未核查。最近来源检查:2026-11-09T15:01:40.427Z。

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