Hugging Face Transformers
Transformers is a model-definition framework for state-of-the-art machine learning with text, vision, audio, video, and multimodal models, supporting both inference and training. It provides a unified API to use thousands of pretrained models from the Hugging Face Hub, simplifying integration across frameworks. It is for developers and researchers who need easy access to cutting-edge models.
🎬 Getting Started With Hugging Face in 15 Minutes | Transformers, Pipeline, Tokenizer, Models · AssemblyAI
✨ Key features
- Unified API for inference and training across modalities.
- Access to 1M+ pretrained model checkpoints on the Hub.
- Supports PyTorch, JAX, and TF2.0 frameworks.
- High-level Pipeline API for quick inference.
- Model definitions compatible with many training and inference tools.
- Easy customization and model sharing.
🎯 Use cases
- Text generation, summarization, translation, and classification.
- Image classification, object detection, and segmentation.
- Automatic speech recognition and text-to-speech.
- Visual question answering and multimodal chat.
- Rapid prototyping with state-of-the-art models.
📦 Installation
🧰 Requirements: Python 3.10+ and PyTorch 2.5+ are required.
python -m venv .my-env
source .my-env/bin/activateuv venv .my-env
source .my-env/bin/activatepip install "transformers[torch]"uv pip install "transformers[torch]"For the latest source version:
git clone https://github.com/huggingface/transformers.git
cd transformers
pip install '.[torch]'uv pip install '.[torch]'🚀 Usage
from transformers import pipeline
# Text generation
pipe = pipeline(task="text-generation", model="Qwen/Qwen2.5-1.5B")
pipe("the secret to baking a really good cake is ")
# Chat with a model
chat = [
{"role": "system", "content": "You are a sassy, wise-cracking robot as imagined by Hollywood circa 1986."},
{"role": "user", "content": "Hey, can you tell me any fun things to do in New York?"}
]
pipe = pipeline(task="text-generation", model="meta-llama/Meta-Llama-3-8B-Instruct", dtype=torch.bfloat16, device_map="auto")
response = pipe(chat, max_new_tokens=512)
print(response[0]["generated_text"][-1]["content"])
⚠️ Good to know
The library is not a modular toolbox of building blocks; model code is not refactored with extra abstractions, and example scripts may require adaptation for specific use cases.
❓ FAQ
What Python and PyTorch versions are required?
Transformers works with Python 3.10+ and PyTorch 2.5+.
How can I install Transformers?
You can install via pip or uv with pip install "transformers[torch]" or uv pip install "transformers[torch]" after creating a virtual environment.
What is the Pipeline API?
The Pipeline is a high-level inference class that supports text, audio, vision, and multimodal tasks, handling preprocessing and returning outputs.
Can I use models with different frameworks?
Yes, Transformers supports PyTorch, JAX, and TF2.0, and you can move a single model between these frameworks.
📊 Repository
🤖 Overview, features, install steps and FAQ were generated from the project's README on Sep 4, 2026. Always check the original source before running commands.