Hugging Face Model Score Curation at Endor Labs

Endor Labs has expanded its Open Source Software discovery capabilities to include AI models from Hugging Face, enabling developers to evaluate models based on activity, popularity, security, and quality. Hugging Face is a platform offering tools and pre-trained models for natural language processing and machine learning, along with APIs that facilitate model experimentation and deployment. Endor Labs' scoring system uses model metadata from Hugging Face, categorizing it into security, activity, popularity, and code quality to assess risk. The system also employs Large Language Models (LLMs) to extract crucial information from model READMEs, despite challenges in parsing due to diverse document formats and writing styles. LLMs are trained to respond with structured data to enhance score accuracy, although limitations in response accuracy and unpredictability remain. Endor Labs acknowledges the trust issue inherent in self-reported metadata, as verification of claims like training datasets or performance results requires deploying the model for testing. The blog suggests that building a trustworthy inventory of model capabilities is a task for future exploration.

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