> 🔮 This version of Thinc has been rewritten from the ground up and will be used to power the upcoming [spaCy v3.0](https://nightly.spacy.io). The new Thinc v8.0 is a **lightweight deep learning library** that offers an elegant, type-checked, functional-programming API for **composing models**, with support for layers defined in other frameworks such as **PyTorch**, **TensorFlow** or **MXNet**. You can use Thinc as an interface layer, a standalone toolkit or a flexible way to develop new models. **For more details, see the [documentation](https://thinc.ai).**
✨ New features and improvements
- **Use any framework:** Switch between PyTorch, TensorFlow and MXNet models without changing your application, or even create mutant hybrids using zero-copy array interchange.
- **Type checking:** Develop faster and catch bugs sooner with sophisticated type checking. Trying to pass a 1-dimensional array into a model that expects two dimensions? That’s a type error. Your editor can pick it up as the code leaves your fingers.
- **Config system:** Configuration is a major pain for ML. Thinc lets you describe trees of objects with references to your own functions, so you can stop passing around blobs of settings. It's simple, clean, and it works for both research and production.
- **Super lightweight:** Small and easy to install with very few required dependencies, available on pip and conda for Linux, macOS and Windows. Simple source with a consistent API.
- Concise functional-programming approach to model definition using composition rather than inheritance.
- First-class support for variable-length sequences: multiple built-in sequence representations and your layers can use any object.