model-sourcing

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Vet the concrete third-party model a study will be built on — this repository, this revision, this checkpoint — not the architecture family. Records a model dossier (source and version pin, licence and the file it was read from, intended use, pretrained-weight provenance, model task vs study task, reported validation, what the model was developed on, your evaluation arms) and gates it deterministically. Catches what a licence check and a citation count cannot: an evaluation arm sitting on the benchmark the model was developed or tuned on, so the arm reads like validation while being closer to a training-set score. Also an evaluation set inside a pretraining corpus, an unstated or use-incompatible licence, an unpinned revision, and a hardware claim never executed. It vets an artifact; it never downloads or runs one.

AI & Automation 292 stars 71 forks Updated 4 days ago MIT

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Skill Content

# Model-Sourcing Skill ## Purpose `/architecture-zoo` answers a literature question — which family of model suits this task. That question has a stable answer. The next question does not: *which concrete artifact do I run?* A repository, a revision, a checkpoint. That is a provenance question, and the two facts a careful researcher usually checks are the two that cannot answer it. The licence tells you whether you may use it. The citation count tells you whether others did. Neither tells you **whether the number you are about to report means what you will say it means.** The failure this skill exists for is the quietest one in the lane. A method developed and tuned against a benchmark family gets evaluated by the next person *on that same family*, and the resulting figure reads like validation while sitting much closer to a training-set score. Nothing in the repository says so. The licence is clean, the paper is peer-reviewed and highly cited, the task matches, the code runs on your GPU. The conflict lives in the **relationship** between two facts that are documented in different places — what the model was developed on, and what you are about to evaluate it on — and it becomes visible only when they are written down side by side. Writing them down side by side is what the dossier is for. ## When to use - You have a concrete candidate (a GitHub repo, a Hugging Face checkpoint, a paper's released weights) and are about to build a study on it. - You are writing the Meth...

Details

Author
Aperivue
Repository
Aperivue/medsci-skills
Created
5 months ago
Last Updated
4 days ago
Language
Python
License
MIT

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Data & Documents Featured

architecture-zoo

Choose a model architecture for a medical-imaging research question before scaffolding. Maps the task (classification, segmentation, detection, transfer), modality and dimensionality, labelled-data scale, and class imbalance to a shortlist of architectures, each grounded in its source paper with a when-to-use, a medical-imaging use, a reference implementation, the typical validation setup, and the matching model-scaffold template. Covers the foundational curriculum (ResNet, DenseNet, EfficientNet, ViT, Swin; U-Net, 3-D U-Net, Attention/Residual U-Net, nnU-Net (+ ResEnc/MedNeXt/STU-Net), Mask R-CNN; SAM/MedSAM, nnInteractive/VISTA3D interactive-3D, TotalSegmentator, BiomedCLIP, DINO/MAE/SimCLR; and graph neural nets — GCN/GraphSAGE/GAT/GIN/BrainGNN — for brain connectomes). It teaches archetypes and the task-to-architecture logic (including the "scale the CNN, new≠better" rigour caveat), not a live SOTA leaderboard.

292 Updated 4 days ago
Aperivue
AI & Automation Listed

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Deep security evaluation of an AI model artifact — a public HuggingFace model (especially custom-code / trust_remote_code models) OR a model folder already on disk. Runs a deterministic collector, authors a dual-pass behavioral claim and checks it against the code's actual call graph, traces generation-to-sink paths, and produces a plain-English report (decision on top, evidence below) with an audit-opinion verdict. Use when asked to evaluate, audit, vet, or decide whether to adopt a model that ships custom code, or after model-scorecard returns tier C or D. EXECUTES NOTHING from the artifact and NEVER issues a clearance; a code-execution artifact seen static-only receives a "disclaimer of opinion".

1 Updated 1 months ago
fastedd27
AI & Automation Solid

chemistry-a-cut-variant-takes-its-analyses-with-it

Use at literature survey, at study design, at every descope decision and again at writing, when the source's method is a family — the same module dropped into two or more backbones, or one architecture published in several named variants — and you are about to run only one of them. Covers listing which of the source's downstream analyses were produced from which variant before any of them is cut, shrinking a variant rather than deleting it, and what a saliency map, case study or ablation computed on the surviving variant is and is not evidence for.

805 Updated 2 weeks ago
tangxiangru