Hugging Face Transformers

Transformers is the model-definition framework for state-of-the-art machine learning models across text, vision, audio, and multimodal domains. It serves as the central pivot that makes model definitions compatible across training frameworks, inference engines, and modeling libraries.

Key Features

• Universal model compatibility - Works with PyTorch, TensorFlow, JAX, and major training/inference frameworks
• 1M+ pre-trained models - Access to massive model hub with checkpoints for any domain
• Multi-modal support - Text, vision, audio, video, and multimodal model architectures
• Pipeline API - High-level interface for quick inference across different tasks
• Framework interoperability - Model definitions work across vLLM, DeepSpeed, TGI, llama.cpp, and more

Installation

# Basic installation
pip install "transformers[torch]"

# With uv (faster)
uv pip install "transformers[torch]"

# From source (latest features)
git clone https://github.com/huggingface/transformers.git
cd transformers
pip install '.[torch]'

Basic Usage

The Pipeline API handles preprocessing and provides a simple interface for inference:

from transformers import pipeline

# Text generation
generator = pipeline(task="text-generation", model="Qwen/Qwen2.5-1.5B")
result = generator("the secret to baking a really good cake is")
print(result[0]['generated_text'])

# Other tasks work similarly
classifier = pipeline("sentiment-analysis")
classifier("This framework is excellent!")

Notable Details

• License: Apache-2.0
• Language: Python
• Community: 158k+ GitHub stars, massive ecosystem adoption
• Ecosystem: Compatible with Axolotl, Unsloth, vLLM, SGLang, TGI, and dozens of other tools