transformers

transformers is a speech (TTS/STT) tool - 🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.

Overview

Transformers acts as the model-definition framework for state-of-the-art machine learning with text, computer vision, audio, video, and multimodal models, for both inference and training.

It centralizes the model definition so that this definition is agreed upon across the ecosystem. 'transformers' is the pivot across frameworks: if a model definition is supported, it will be compatible with the majority of training frameworks (Axolotl, Unsloth, DeepSpeed, FSDP, PyTorch-Lightning, ...), inference engines (vLLM, SGLang, TGI, ...), and adjacent modeling libraries (llama.cpp, mlx, ...) which leverage the model definition from 'transformers'.

We pledge to help support new state-of-the-art models and democratize their usage by having their model definition be simple, customizable, and efficient.

There are over 1M+ Transformers model checkpoints on the Hugging Face Hub you can use.

Explore the Hub today to find a model and use Transformers to help you get started right away.

Create and activate a virtual environment with venv or uv, a fast Rust-based Python package and project manager.

Install Transformers from source if you want the latest changes in the library or are interested in contributing. However, the latest version may not be stable. Feel free to open an issue if you encounter an error.

transformers is open-source, written primarily in Python, with 166,737 GitHub stars under the Apache 2.0 license. The latest release is v5.17.0 (2026-09-09).

Key capabilities

From the project's documentation:

• High performance on natural language understanding & generation, computer vision, audio, video, and multimodal tasks.
• Low barrier to entry for researchers, engineers, and developers.
• Few user-facing abstractions with just three classes to learn.
• A unified API for using all our pretrained models.
• Share trained models instead of training from scratch.
• Reduce compute time and production costs.

Install

A quick way to get started (always check the official docs for the latest):

pip install "transformers[torch]"

How it fits a local-AI stack

transformers runs on your own hardware, so pair it with a model and a GPU sized to your needs. Use the VRAM calculator to pick a model that fits your card, and see what you can run for hardware guidance. Related speech (TTS/STT) tools in the directory:

• "cactus"
• "claude-code-local"
• "dograh"
• "DreamServer"

Sources

• Source code & docs: huggingface/transformers
• Official website: https://huggingface.co/transformers

Stats from GitHub, 2026-09-28.