# transformers - **Canonical URI:** https://www.vioscale.ai/software/transformers - **Category:** AI Tools - **Vendor:** Huggingface - **Homepage:** https://huggingface.co/transformers - **Also known as:** transformers - **Profile claimed by vendor:** no - **Last updated:** 2026-08-05T13:14:51.943Z ## Vioscale score **77.3 / 100**, confidence 18% (low). Composite of weighted, independently-sourced signals (no user reviews, no vendor payment). | Signal | Score | Weight | Contribution | Evidence present | |---|--:|--:|--:|:--:| | price_level | 100 | 0.058 | 5.8 | ✓ | | reliability | 50 | 0.07 | 3.5 | ✓ | | capabilities | 0 | 0.187 | 0 | - | | github_stars | 98.3 | 0.029 | 2.9 | ✓ | | integrations | 0 | 0.105 | 0 | - | | github_activity | 63.1 | 0.105 | 6.6 | ✓ | | release_cadence | 98.3 | 0.058 | 5.7 | ✓ | | security_posture | 10 | 0.07 | 0 | - | | package_downloads | 0 | 0.152 | 0 | - | | pricing_transparency | 80 | 0.094 | 7.5 | ✓ | | stackoverflow_activity | 0 | 0.07 | 0 | - | ## Pricing _As of 2026-08-05, [verify at source](https://huggingface.co/transformers). Independently observed._ Open source · Free tier ## About Transformers acts as the model-definition framework for state-of-the-art machine learning models in text, computer vision, audio, video, and multimodal models, for both inference. _Independently observed._ ## Platform & deployment - **Platforms:** CLI - **Deployment:** Cloud / SaaS ## FAQ ### What is Transformers and what is it used for? Transformers is an open-source model-definition framework for building and deploying state-of-the-art machine learning models. It provides infrastructure for working with models across text, computer vision, audio, video, and multimodal domains, making it suitable for diverse machine learning tasks. _Source: [https://huggingface.co/transformers](https://huggingface.co/transformers)._ ### What types of models and domains does Transformers support? Transformers covers models for text processing, computer vision, audio processing, video understanding, and multimodal applications. This breadth allows developers to work with machine learning models across different data types and use cases in a single framework. _Source: [https://huggingface.co/transformers](https://huggingface.co/transformers)._ ### What is the cost of using Transformers? Transformers is open-source software with no licensing fees or usage costs. It is completely free to download, use, and deploy in any environment. _Source: [https://huggingface.co/transformers](https://huggingface.co/transformers)._ ### How do I access and use Transformers? Transformers is available through both web-based and command-line interfaces, allowing you to choose the access method that fits your workflow. This dual-access approach accommodates different working styles and integration patterns. _Source: [https://huggingface.co/transformers](https://huggingface.co/transformers)._ ### Can I run Transformers on my own infrastructure? Yes, Transformers supports command-line usage, enabling you to install and run it on your own systems and infrastructure. This gives you full control over deployment, data handling, and resource allocation. _Source: [https://huggingface.co/transformers](https://huggingface.co/transformers)._ ### Does Transformers support cloud deployment? Transformers offers cloud deployment options, allowing you to run the framework in cloud environments for scalability and accessibility. This flexibility enables you to move workloads between local and cloud infrastructure as your needs change. _Source: [https://huggingface.co/transformers](https://huggingface.co/transformers)._ ## Facts Every value below carries its source and our confidence. Facts are re-crawled on a freshness schedule. ### language | Attribute | Value | Source | Retrieved | Confidence | |---|---|---|---|---| | language.primary | Python | [link](https://github.com/huggingface/transformers) | 2026-07-31 | 90% (high) | ### license | Attribute | Value | Source | Retrieved | Confidence | |---|---|---|---|---| | license.spdx | Apache-2.0 | [link](https://github.com/huggingface/transformers) | 2026-07-31 | 95% (high) | ### pricing | Attribute | Value | Source | Retrieved | Confidence | |---|---|---|---|---| | pricing.free_tier | yes | [link](https://huggingface.co/transformers) | 2026-08-05 | 60% (medium) | | pricing.model | commercial | [link](https://huggingface.co/transformers) | 2026-08-05 | 60% (medium) | | pricing.price_level | free | [link](https://huggingface.co/transformers) | 2026-08-05 | 60% (medium) | | pricing.transparent | yes | [link](https://huggingface.co/transformers) | 2026-07-31 | 60% (medium) | ### release | Attribute | Value | Source | Retrieved | Confidence | |---|---|---|---|---| | release.cadence_days | 3 | [link](https://github.com/huggingface/transformers/releases) | 2026-07-31 | 70% (medium) | ### activity | Attribute | Value | Source | Retrieved | Confidence | |---|---|---|---|---| | activity.commits_last_30d | 100 | [link](https://github.com/huggingface/transformers/pulse) | 2026-07-31 | 65% (medium) | ### adoption | Attribute | Value | Source | Retrieved | Confidence | |---|---|---|---|---| | adoption.github_stars | 163,201 | [link](https://github.com/huggingface/transformers) | 2026-07-31 | 90% (high) | ### content | Attribute | Value | Source | Retrieved | Confidence | |---|---|---|---|---| | content.faq | `[{"answer":"Transformers is an open-source model-definition framework for building and deploying state-of-the-art machine learning models. It provides infrastructure for working with models across text, computer vision, audio, video, and multimodal domains, making it suitable for diverse machine learning tasks.","source":"https://huggingface.co/transformers","question":"What is Transformers and what is it used for?","confidence":0.95},{"answer":"Transformers covers models for text processing, computer vision, audio processing, video understanding, and multimodal applications. This breadth allows developers to work with machine learning models across different data types and use cases in a single framework.","source":"https://huggingface.co/transformers","question":"What types of models and domains does Transformers support?","confidence":0.95},{"answer":"Transformers is open-source software with no licensing fees or usage costs. It is completely free to download, use, and deploy in any environment.","source":"https://huggingface.co/transformers","question":"What is the cost of using Transformers?","confidence":0.95},{"answer":"Transformers is available through both web-based and command-line interfaces, allowing you to choose the access method that fits your workflow. This dual-access approach accommodates different working styles and integration patterns.","source":"https://huggingface.co/transformers","question":"How do I access and use Transformers?","confidence":0.75},{"answer":"Yes, Transformers supports command-line usage, enabling you to install and run it on your own systems and infrastructure. This gives you full control over deployment, data handling, and resource allocation.","source":"https://huggingface.co/transformers","question":"Can I run Transformers on my own infrastructure?","confidence":0.7},{"answer":"Transformers offers cloud deployment options, allowing you to run the framework in cloud environments for scalability and accessibility. This flexibility enables you to move workloads between local and cloud infrastructure as your needs change.","source":"https://huggingface.co/transformers","question":"Does Transformers support cloud deployment?","confidence":0.9}]` | [link](https://huggingface.co/transformers) | 2026-07-31 | 87% (high) | ### market | Attribute | Value | Source | Retrieved | Confidence | |---|---|---|---|---| | market.availability | `{"primaryMarkets":[],"availabilityScope":"global","availableCountries":[],"notAvailableCountries":[]}` | [link](https://huggingface.co/transformers) | 2026-08-05 | 75% (high) | ### reliability | Attribute | Value | Source | Retrieved | Confidence | |---|---|---|---|---| | reliability.status_page | yes | [link](https://status.huggingface.co) | 2026-08-05 | 60% (medium) | ### security | Attribute | Value | Source | Retrieved | Confidence | |---|---|---|---|---| | security.disclosure_policy | yes | [link](https://huggingface.co/.well-known/security.txt) | 2026-08-05 | 60% (medium) | | security.gdpr | yes | [link](https://huggingface.co/privacy) | 2026-08-05 | 75% (high) | --- *Source: Vioscale (https://www.vioscale.ai/software/transformers). Independent, evidence-based software intelligence. Cite the canonical URI.*