transformers
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.
npx skills add majiayu000/claude-skill-registry --skill scientific-transformers-blurjp-imageprepmcp --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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
- Overview
- Installation
- Authentication
- Quick Start
- Core Capabilities
- 1. Pipelines for Quick Inference
- 2. Model Loading and Management
- 3. Text Generation
- 4. Training and Fine-Tuning
- 5. Tokenization
- Common Patterns
- Pattern 1: Simple Inference
- Pattern 2: Custom Model Usage
- Pattern 3: Fine-Tuning
uv pip install torch transformers datasets evaluate accelerate uv pip install timm pillow uv pip install librosa soundfile export HUGGINGFACE_TOKEN="your_token_here"
What does the transformers skill do?
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.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill scientific-transformers-blurjp-imageprepmcp --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.