Today, as we launch v5, Transformers is installed more than 3 million times each day via pip - up from 20,000/day in v4 🤯. Altogether, it has now surpassed 1.2 billion installs!

The ecosystem has expanded from 40 model architectures in v4 to over 400 today, and the community has contributed more than 750,000 model checkpoints on the Hub compatible with Transformers, up from roughly 1,000 at the time of v4.

This growth is powered by the evolution of the field and the now mainstream access to AI. As a leading model-definition library in the ecosystem, we need to continuously evolve and adapt the library to continue being relevant. Reinvention is key for longevity in AI.

We’re fortunate to collaborate with many libraries and apps built on transformers, in no specific order: llama.cpp, MLX, onnxruntime, Jan, LMStudio, vLLM, SGLang, Unsloth, LlamaFactory, dLLM, MaxText, TensorRT, Argmax, among many other friends.

For v5, we wanted to work on several notable aspects: simplicity, training, inference, and production. We detail the work that went into them in this post.

The first focus of the team was on simplicity. Working on transformers, we see the code as the product. We want our model integrations to be clean, so that the ecosystem may depend on our model definitions and understand what’s really happening under the hood, how models differ from each other, and the key features of each new model. Simplicity results in wider standardization, generality, and wider support.

Transformers is the backbone of hundreds of thousands of projects, Unsloth included.

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