huggingface/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.
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Recorded metrics
stars · 168589 stars
2026-11-06 00:30:30.458Z
cumulative since publication
Research analysis
Project record
Repository: huggingface/transformers. Source account: 🤗 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.
Observed adoption & comparisons
Total stars: 168,589 stars (Nov 6, 2026 UTC; cumulative since publication); previous same-scope observation: 168564 (Nov 5, 2026 UTC). These observations do not establish demand growth. Recent records to compare: “Tencent Launches Hy4, One of the Leading Open-Source AI Models” (Hacker News, Nov 5, 2026); “Show HN: Resilient AI Worker Queue with Redis, BullMQ, and Budget Kill Switches” (Show HN, Nov 5, 2026). Shared themes are navigation hints; they have not been verified as the same need or independent corroboration.
Adoption decision
Evaluate it against the tool or manual workflow it would replace. The stored record supports discovery; it does not yet establish reliability, lower operating cost or commercial adoption.
Unverified requirements
Not verified: license permissions, required hardware, installation steps, maintenance status and a representative task with its failure cases.
Next test
Choose one real task and a current alternative. Record the commit, environment, input, output, elapsed time and failure behavior; retain the result before recommending adoption.