About transformers
🤗 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.
What you should know about transformers
transformers — 🤗 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.. It is categorized under AI Tools and primarily built with Python. The project has gathered 159,454 stars and 32,883 forks on GitHub, indicating strong adoption among developers.
Pricing & licensing: This tool is offered free of charge , released under the Apache-2.0 license. The source code is openly available on GitHub, allowing engineers to audit, contribute, or fork as needed.
Use cases & topics: transformers is associated with the following topics: audio, deep-learning, deepseek, gemma, glm, hacktoberfest, llm, machine-learning. Teams working in audio / deep-learning / deepseek spaces typically evaluate this kind of tool when scoping new architecture decisions or replacing legacy components.
Getting started: Check out the official GitHub repository for installation steps, configuration examples, and the latest release notes. Most teams hit value within the first week if the tool aligns with their existing AI Tools stack.
Editor's note from Fanny Engriana (Founder, Wardigi Digital Agency): when evaluating tools in the AI Tools category for our agency clients, we look at three things first — license clarity, community size, and active maintenance. Tools with explicit license terms and ongoing commits tend to remain viable across multi-year projects.