Tabular Regression
PyTorch
canis-m
chemistry
science
physics
quantum
quantum-computing
physical-ai
drug-discovery
cheminformatics
molecules
quantum-chemistry
computational-chemistry
electronic-structure
molecular-property
property-prediction
correlation-energy
delta-learning
casci
mp2
quantum-token
quantum-tokens
Eval Results (legacy)
Commit History
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Add config.json: model spec for discovery and download counting (#1)
- Add config.json: model spec for discovery and download counting (c5144065f65d4785d2377ecdaa2b560c6744eca6)78f62ff
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cards: minting for your own models
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cards: two contact lines, real addresses
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cards: tokens@ for TokenService
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cards: contact as plain lines
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cards: one runnable block per page
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card: one chart that renders everywhere
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card: name the NVIDIA hardware behind the drug-scale labels
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cards: pre-release review fixes
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card: what the user needs, not how we think about it
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card: the licences, without the commentary
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card: the pack carries its own licence
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card: gated access, and the pack's real coverage
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card: the free token pack, the drug-scale panel, no em dashes
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card: cite the posted ChemRxiv preprint (10.26434/chemrxiv.15009119)
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card: correct the 3D-structure margin to the measured 1.9-2.9x, file size, torsion row, drug-size limitation; cite the ChemRxiv preprint
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Canis M v1: weights, card, assets
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Canis M v1: weights, card, assets
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card: one copy-paste quickstart, verified from a clean environment
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card: link the token pack, so the weights are runnable on arrival
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card: drop the mark, it does not render
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Canis M v1: weights, card, assets
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initial commit
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