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Facts
| Fact | PathologyFoundation/plip Pathology Language and Image Pre-Training (PLIP) is the first vision and language foundation model for Pathology AI (Nature Medicine). PLIP is a large-scale pre-trained model that can be used to extract visual and language features from pathology images and text description. The model is a fine-tuned version of the original CLIP model. | jingyaogong/minimind 🧠 Train a 64M-parameter LLM from scratch in just 2h! |
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| Stars | 383 | 64,875 |
| Net stars, last 30 days | +3 | +1,768 |
| Largest star day | +2 on Jun 16, 2026 | +912 on Sep 1, 2026 |
| Milestone reached in the record | None | 50,000 on May 26, 2026 |
| Forks | 39 | 8,487 |
| Language | Python | Python |
| License | No license | APACHE-2.0 |
| Created | Feb 15, 2023 | Jul 27, 2024 |
| Last push | Sep 20, 2023 | Oct 27, 2026 |
| Archived | No | No |
| Health facts: Maintenance | 1 of 4 checks (2 without data) | 4 of 4 checks |
| Health facts: Releases | 0 of 3 checks (2 without data) | 0 of 3 checks |
| Health facts: Security practice | 0 of 1 check (1 without data) | 0 of 1 check |
| Health facts: Community files | 0 of 3 checks (2 without data) | 2 of 3 checks |
| Commits, last 13 weeks | No data | 60 |
| Weeks with commits, last 52 | No data | 37 of 52 |
| In the Repository Index since | Oct 22, 2026 | Oct 22, 2026 |
Topics that all of them have on the Repository Host: artificial-intelligence. The facts come from the Repository Host and from the Star History that AwesomeRepos records. A shared topic does not mean that one repository can replace another. Each Health Facts row is one dimension alone. AwesomeRepos does not add them into one score. How each check is defined