Rare AI Archive
A curated, open-weight collection of language models fine-tuned on rare disease literature — from diagnostic reasoning to patient advocacy.
The Rare AI Archive hosts open-weight models trained on the intersection of rare disease research and patient experience. Every model ships with a lineage card tracing its training data and evaluation metrics.
Built with the Wilhelm Foundation, the archive is a complete open-source rare disease diagnostic AI toolkit on HuggingFace. The centerpiece is rare-archive-qwen-4b-sft-v1 — a 4-billion parameter language model fine-tuned on rare disease clinical reasoning. Three companion evaluation datasets provide standardized benchmarks, and a clinical demo Space lets physicians test diagnostic reasoning against ten real-world scenarios.
The tagline says it: “No disease is too rare to matter.” Open weights mean any researcher, anywhere, can download the model and run it on their own infrastructure. No API keys. No usage limits. No gatekeepers between a clinician’s question and a tool that might help answer it.
Behind every dataset is a patient. Behind every model is hope. The Rare AI Archive is what happens when you take that seriously and ship the infrastructure to match.