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Hugging Face

As explained in Software Testing with Generative AI

Hugging Face is a platform and community hub for sharing, accessing, training, and documenting machine-learning models and datasets.

Hugging Face is a community hub rather than a proprietary model provider. It gathers a wide range of mostly open-source models from companies and research labs, alongside datasets, apps, and documentation. section 2.5.2

The platform gives practitioners access to pretrained models and resources for adapting or further training them for specific problems. Choosing a model begins with the task and its practical criteria, not with tracking every new release. section 2.5.2

Exploring available models

The models index contains hundreds of thousands of differently trained models, allowing users to compare available options before selecting one for a particular problem. section 2.5.2

How Software Testing with Generative AI covers it

How deep it goes
Defines the platform’s resources and selection rationale but defers detailed model adaptation and further training.
Examples
Uses a models index containing hundreds of thousands of differently trained models.
What you can do after
Apply via Hugging Face by finding pretrained models and related resources for a specific machine-learning problem.
How it connects
Connects open-source models with datasets, apps, documentation, pretrained models, and model training.

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This explainer was generated by Manning's knowledge graph from the text of Software Testing with Generative AI. The section links point to the parts of the book it draws on.